Artificial Intelligence: Case-based & Model-based Reasoning
•Download as PPT, PDF•
11 likes•12,739 views
This presentation covers case-based and model-based reasoning for artificial intelligence. Topics covered are as follows: case-based reasoning, case-based reasoning components; case base, retriever, adapter, refiner, executor, and evalutator; and model-based reasoning.
Report
Share
Report
Share
Recommended
Multilayer & Back propagation algorithm
The document discusses multilayer neural networks and the backpropagation algorithm. It begins by introducing sigmoid units as differentiable threshold functions that allow gradient descent to be used. It then describes the backpropagation algorithm, which employs gradient descent to minimize error by adjusting weights. Key aspects covered include defining error terms for multiple outputs, deriving the weight update rules, and generalizing to arbitrary acyclic networks. Issues like local minima and representational power are also addressed.
Fuzzy expert system
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
Forward and Backward chaining in AI
The inference engine applies logical rules to facts in the knowledge base to infer new information. It uses two approaches:
- Forward chaining starts with known facts and fires rules until reaching the goal, applying rules in a bottom-up manner.
- Backward chaining starts with the goal and works backwards through rules to find supporting facts, taking a top-down approach.
Both are illustrated using examples of determining an animal's color. Forward chaining applies rules to known facts about an animal to conclude its color, while backward chaining starts with the color goal and applies rules in reverse to find facts proving the goal.
Propositional logic
This document discusses propositional logic and knowledge representation. It introduces propositional logic as the simplest form of logic that uses symbols to represent facts that can then be joined by logical connectives like AND and OR. Truth tables are presented as a way to determine the truth value of propositions connected by these logical operators. The document also discusses concepts like models of formulas, satisfiable and valid formulas, and rules of inference like modus ponens and disjunctive syllogism that allow deducing new facts from initial propositions. Examples are provided to illustrate each concept.
Planning in AI(Partial order planning)
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Frames
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Semantic net in AI
1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Recommended
Multilayer & Back propagation algorithm
The document discusses multilayer neural networks and the backpropagation algorithm. It begins by introducing sigmoid units as differentiable threshold functions that allow gradient descent to be used. It then describes the backpropagation algorithm, which employs gradient descent to minimize error by adjusting weights. Key aspects covered include defining error terms for multiple outputs, deriving the weight update rules, and generalizing to arbitrary acyclic networks. Issues like local minima and representational power are also addressed.
Fuzzy expert system
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
Forward and Backward chaining in AI
The inference engine applies logical rules to facts in the knowledge base to infer new information. It uses two approaches:
- Forward chaining starts with known facts and fires rules until reaching the goal, applying rules in a bottom-up manner.
- Backward chaining starts with the goal and works backwards through rules to find supporting facts, taking a top-down approach.
Both are illustrated using examples of determining an animal's color. Forward chaining applies rules to known facts about an animal to conclude its color, while backward chaining starts with the color goal and applies rules in reverse to find facts proving the goal.
Propositional logic
This document discusses propositional logic and knowledge representation. It introduces propositional logic as the simplest form of logic that uses symbols to represent facts that can then be joined by logical connectives like AND and OR. Truth tables are presented as a way to determine the truth value of propositions connected by these logical operators. The document also discusses concepts like models of formulas, satisfiable and valid formulas, and rules of inference like modus ponens and disjunctive syllogism that allow deducing new facts from initial propositions. Examples are provided to illustrate each concept.
Planning in AI(Partial order planning)
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Frames
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Semantic net in AI
1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Register allocation and assignment
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
Knowledge Representation in Artificial intelligence
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
Semantic Networks
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Rule based system
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
Agents in Artificial intelligence
The document discusses different types of intelligent agents based on their architecture and programs. It describes five classes of agents: 1) Simple Reflex agents which react solely based on current percepts; 2) Model-based agents which use an internal model of the world; 3) Goal-based agents which take actions to reduce distance to a goal; 4) Utility-based agents which choose actions to maximize utility; and 5) Learning agents which are able to learn from experiences. The document also outlines different forms of learning like rote learning, learning from instruction, and reinforcement learning.
Artificial Intelligence Approaches
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
AI_Session 7 Greedy Best first search algorithm.pptx
This document summarizes key topics from a session on problem solving by search algorithms in artificial intelligence. It discusses uninformed search strategies like breadth-first search and depth-first search. It also covers informed, heuristic search strategies such as greedy best-first search and A* search which use heuristic functions to estimate distance to the goal. Examples are provided to illustrate best first search, and it describes how this algorithm expands nodes and uses priority queues to order nodes by estimated cost. The next session is slated to cover the A* search algorithm in more detail.
AI Lecture 7 (uncertainty)
This document discusses uncertainty and probability theory. It begins by explaining sources of uncertainty for autonomous agents from limited sensors and an unknown future. It then covers representing uncertainty with probabilities and Bayes' rule for updating beliefs. Examples show inferring diagnoses from symptoms using conditional probabilities. Independence is described as reducing the information needed for joint distributions. The document emphasizes probability theory and Bayesian reasoning for handling uncertainty.
knowledge representation using rules
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
Chaptr 7 (final)
The document discusses different types of machine learning, including rote learning, learning by taking advice, and learning through problem solving. Rote learning involves simply storing examples to improve future performance, as demonstrated in Samuel's checkers program. Learning by taking advice requires translating human advice into operational rules a program can understand and apply, as shown through FOO's hearts-playing program. Learning through problem solving involves adjusting parameter weights based on success, as Samuel's program did by modifying evaluation function coefficients over time based on game outcomes.
Knowledge representation In Artificial Intelligence
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Defuzzification
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
8. mutual exclusion in Distributed Operating Systems
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
PAC Learning
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
strong slot and filler
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
Issues in knowledge representation
Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.
Logics for non monotonic reasoning-ai
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
search strategies in artificial intelligence
The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.
Truth management system
The document provides an overview of Truth Maintenance Systems (TMS) in artificial intelligence. It discusses key aspects of TMS including:
1. Enforcing logical relations among beliefs by maintaining and updating relations when assumptions change.
2. Generating explanations for conclusions by using cached inferences to avoid re-deriving inferences.
3. Finding solutions to search problems by representing problems as sets of variables, domains, and constraints.
The document also covers justification-based and assumption-based TMS, and how a TMS interacts with a problem solver to add and retract assumptions, detect contradictions, and perform belief revision.
Distributed & parallel system
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Chapter 4.ppt mizan Tepi university student population
This document discusses different types of knowledge application systems and technologies that enable them. It describes rule-based expert systems and case-based reasoning as two important intelligent technologies. Rule-based systems represent domain knowledge as IF-THEN production rules, while case-based reasoning solves new problems by adapting solutions from similar past cases. The document also outlines other reasoning approaches like constraint-based, model-based, and diagrammatic reasoning. Finally, it identifies help desk systems and organizational policies/standards as examples of knowledge application systems.
System Simulation and Modelling with types and Event Scheduling
System simulation and modelling involves creating models of real-world systems and using those models to simulate and analyze the performance of existing or proposed systems. A system consists of interrelated components that work together towards a common goal. Simulation is the process of using a model to study the performance of a system over time or space. Modelling involves creating a model that represents a system, while simulation operates that model. Simulation can be used across various domains like healthcare, engineering, and military applications. It provides advantages like testing changes without impacting real systems and identifying constraints.
More Related Content
What's hot
Register allocation and assignment
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
Knowledge Representation in Artificial intelligence
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
Semantic Networks
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Rule based system
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
Agents in Artificial intelligence
The document discusses different types of intelligent agents based on their architecture and programs. It describes five classes of agents: 1) Simple Reflex agents which react solely based on current percepts; 2) Model-based agents which use an internal model of the world; 3) Goal-based agents which take actions to reduce distance to a goal; 4) Utility-based agents which choose actions to maximize utility; and 5) Learning agents which are able to learn from experiences. The document also outlines different forms of learning like rote learning, learning from instruction, and reinforcement learning.
Artificial Intelligence Approaches
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
AI_Session 7 Greedy Best first search algorithm.pptx
This document summarizes key topics from a session on problem solving by search algorithms in artificial intelligence. It discusses uninformed search strategies like breadth-first search and depth-first search. It also covers informed, heuristic search strategies such as greedy best-first search and A* search which use heuristic functions to estimate distance to the goal. Examples are provided to illustrate best first search, and it describes how this algorithm expands nodes and uses priority queues to order nodes by estimated cost. The next session is slated to cover the A* search algorithm in more detail.
AI Lecture 7 (uncertainty)
This document discusses uncertainty and probability theory. It begins by explaining sources of uncertainty for autonomous agents from limited sensors and an unknown future. It then covers representing uncertainty with probabilities and Bayes' rule for updating beliefs. Examples show inferring diagnoses from symptoms using conditional probabilities. Independence is described as reducing the information needed for joint distributions. The document emphasizes probability theory and Bayesian reasoning for handling uncertainty.
knowledge representation using rules
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
Chaptr 7 (final)
The document discusses different types of machine learning, including rote learning, learning by taking advice, and learning through problem solving. Rote learning involves simply storing examples to improve future performance, as demonstrated in Samuel's checkers program. Learning by taking advice requires translating human advice into operational rules a program can understand and apply, as shown through FOO's hearts-playing program. Learning through problem solving involves adjusting parameter weights based on success, as Samuel's program did by modifying evaluation function coefficients over time based on game outcomes.
Knowledge representation In Artificial Intelligence
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Defuzzification
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
8. mutual exclusion in Distributed Operating Systems
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
PAC Learning
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
strong slot and filler
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
Issues in knowledge representation
Knowledge representation techniques face several issues including representing important attributes of objects, relationships between attributes, choosing the level of detail in representations, depicting sets of multiple objects, and determining appropriate structures as needed.
Logics for non monotonic reasoning-ai
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
search strategies in artificial intelligence
The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.
Truth management system
The document provides an overview of Truth Maintenance Systems (TMS) in artificial intelligence. It discusses key aspects of TMS including:
1. Enforcing logical relations among beliefs by maintaining and updating relations when assumptions change.
2. Generating explanations for conclusions by using cached inferences to avoid re-deriving inferences.
3. Finding solutions to search problems by representing problems as sets of variables, domains, and constraints.
The document also covers justification-based and assumption-based TMS, and how a TMS interacts with a problem solver to add and retract assumptions, detect contradictions, and perform belief revision.
Distributed & parallel system
The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
What's hot (20)
Knowledge Representation in Artificial intelligence
Knowledge Representation in Artificial intelligence
AI_Session 7 Greedy Best first search algorithm.pptx
AI_Session 7 Greedy Best first search algorithm.pptx
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial Intelligence
8. mutual exclusion in Distributed Operating Systems
8. mutual exclusion in Distributed Operating Systems
Similar to Artificial Intelligence: Case-based & Model-based Reasoning
Chapter 4.ppt mizan Tepi university student population
This document discusses different types of knowledge application systems and technologies that enable them. It describes rule-based expert systems and case-based reasoning as two important intelligent technologies. Rule-based systems represent domain knowledge as IF-THEN production rules, while case-based reasoning solves new problems by adapting solutions from similar past cases. The document also outlines other reasoning approaches like constraint-based, model-based, and diagrammatic reasoning. Finally, it identifies help desk systems and organizational policies/standards as examples of knowledge application systems.
System Simulation and Modelling with types and Event Scheduling
System simulation and modelling involves creating models of real-world systems and using those models to simulate and analyze the performance of existing or proposed systems. A system consists of interrelated components that work together towards a common goal. Simulation is the process of using a model to study the performance of a system over time or space. Modelling involves creating a model that represents a system, while simulation operates that model. Simulation can be used across various domains like healthcare, engineering, and military applications. It provides advantages like testing changes without impacting real systems and identifying constraints.
LPP application and problem formulation
Operational research models can help organizations in various sectors. Some key examples include:
1) British Telecom used an OR model to schedule over 40,000 field engineers, saving $150 million annually from 1997-2000.
2) Continental Airlines developed a crew scheduling model to help resume normal operations just days after 9/11.
3) Ford Motor Company reduced annual prototype costs by $250 million using an optimization model to share prototype vehicles between testing needs.
Machine Learning Methods 2.pptx
The document discusses different machine learning methods including supervised learning, unsupervised learning, case-based reasoning, and genetic algorithms. Supervised learning involves predicting outputs from inputs using labeled training data, while unsupervised learning discovers patterns in unlabeled data. Case-based reasoning solves new problems by adapting solutions to similar past cases, and genetic algorithms find optimal solutions by evolving candidate solutions using techniques inspired by biological evolution.
Lecture01.ppt
This document provides an introduction to an advanced algorithms analysis and design course taught by Dr. Nazir Ahmad Zafar. It outlines Dr. Zafar's educational background and the objectives of the course, which are to design and analyze modern algorithms, compare their efficiencies, and apply them to real-world problems. The course will cover topics like computational complexity, graph theory, string matching, and NP-completeness. The first lecture will introduce algorithms, design techniques, models of computation, and discuss the importance of algorithms.
Recommender Systems.pptx
Finance: Boosting is used with deep learning models to automate critical tasks, including fraud detection, pricing analysis, and more. For example, boosting methods in credit card fraud detection and financial products pricing analysis (link resides outside of ibm.com) improve the accuracy of analyzing massive data sets to minimize financial losses.
Unit 1 introduction to simulation
Simulation involves developing a model of a real-world system over time to analyze its behavior and performance. The key aspects covered in this document include defining simulation as modeling the operation of a system over time through artificial history generation and observation. Simulation models can be used as analysis and design tools to predict the effects of changes to a system before actual implementation. Discrete event simulation is discussed as a common technique that models systems with state changes occurring at discrete points in time. The document also outlines the steps in a typical simulation study including problem formulation, model conceptualization, experimentation and analysis.
Lecture 6 expert systems
Expert systems use rules and heuristics to solve problems in specific domains like medicine or engineering. They operate on incomplete information and provide recommendations rather than exact answers. Rule-based expert systems consist of a knowledge base containing IF-THEN rules, a working memory of facts, and an inference engine that applies rules to derive new facts or recommendations. The inference engine matches rules to facts, selects rules to fire using conflict resolution strategies, and repeats the process until a solution or termination criteria is reached. Managing uncertainty is important, and can be done using probability, fuzzy logic, or other methods. Hybrid systems integrate techniques like neural networks, fuzzy systems, and evolutionary computation.
Parallel Rule Generation For Efficient Classification System
Parallel Rule Generation For Efficient Classification System,
genetic algorithms,
divide and conquer approach to classification , Distributed computing to solve classification problem , heterogeneous approach to classification
Experimental Design for Distributed Machine Learning with Myles Baker
This document discusses experimental design for distributed machine learning models. It outlines common problems in machine learning modeling like selecting the best algorithm and evaluating a model's expected generalization error. It describes steps in a machine learning study like collecting data, building models, and designing experiments. The goal of experimentation is to understand how model factors affect outcomes and obtain statistically significant conclusions. Techniques discussed for analyzing distributed model outputs include precision-recall curves, confusion matrices, and hypothesis testing methods like the chi-squared test and McNemar's test. The document emphasizes that experimental design for distributed learning poses new challenges around data characteristics, computational complexity, and reproducing results across models.
IIE Conference Presentation
Brandon Theiss presented on how quality decisions require quality data. He discussed limitations of traditional data collection methods and provided case studies showing how new technologies enabled real-time data collection. This improved process understanding and quality. For example, adding scales and scanners to production lines eliminated transcription errors and provided visibility into issues. The key is linking data collection to business goals and using tools like Lean Six Sigma to reveal truths from quality data.
Machine Learning
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
'A critique of testing' UK TMF forum January 2015
This presentation draws upon the 'Critique of Testing' Ebook that was discussed at January's UK TMF forum. The slides explore the fundamental concepts of test case design and provide a detailed analysis of each method in terms of them.
Mining Performance Regression Testing Repositories for Automated Performance ...
This document proposes a technique to automate the analysis of performance regression tests by deriving performance signatures from historical data and comparing new tests against these signatures. It describes challenges with current manual analysis practices, including the large number of metrics, lack of up-to-date performance objectives, and subjectivity of analysts. The proposed technique involves normalizing metrics, discretizing values, deriving signatures from past tests, and comparing signatures to detect regressions. Case studies on open source and enterprise systems show the technique can precisely detect 75-100% of regressions and recall 52-67% compared to manual analysis.
Bse 3105 lecture 3-software evolution planning
1. Evolution planning involves justifying an approach to transforming a system through activities like identifying candidate systems for evolution, determining feasibility and costs, and formulating the best strategy.
2. Evolution strategies include continued maintenance, reengineering, or replacement depending on feasibility and costs. Reengineering transforms a system while replacement introduces a new system.
3. Developing an evolution plan requires estimating costs and risks, performing cost-benefit analyses to compare strategies, and planning the process to coordinate resources and activities.
Hpd 1
The document discusses physical database design and tuning. It covers topics such as understanding the workload, index selection guidelines, clustering and indexing tools. Database tuning involves tuning indexes, tuning the conceptual schema such as normalization, and tuning queries. The goal is to refine the initial design based on the actual database usage to improve performance.
AI_Unit-4_Learning.pptx
This document discusses various types of machine learning. It begins by defining learning as changes that enable a system to perform tasks more efficiently over time through constructing or modifying representations of experiences. It then covers several types of learning including rote learning, learning by taking advice, learning through problem solving via parameter adjustment and chunking, learning from examples through induction, explanation based learning, learning through discovery and analogy. It also discusses formal learning theory and neural network and genetic learning. Key aspects of learning systems and different learning paradigms are described.
Requirements analysis lecture
The document discusses requirements analysis and specification. It describes the requirements engineering (RE) process, including elicitation, analysis, specification, and human-machine interface design. It distinguishes between the problem domain, described by requirements documents, and the system to be built, described by specification documents. Requirements analysis involves studying user needs to define system requirements and the problem domain. Objectives include prioritizing requirements and resolving conflicts. Requirements specification defines the behavior of the new system such that it satisfies the problem domain. Models are used throughout requirements analysis and specification to better understand problems and solutions.
The Benefits of using Systems Thinking in Project Management
Systems thinking can provide benefits at multiple stages of project management. It enables a fuller understanding of the problem and solution by considering the overall system. This reduces issues during project execution and allows more focus on communication. The case study showed how Portsmouth Council used systems thinking to reduce housing maintenance times by over 50% and costs by 10% while improving customer satisfaction.
Pressman ch-22-process-and-project-metrics
This document discusses metrics that can be used to measure software processes and projects. It begins by defining software metrics and explaining that they provide quantitative measures that offer insight for improving processes and projects. It then distinguishes between metrics for the software process domain and project domain. Process metrics are collected across multiple projects for strategic decisions, while project metrics enable tactical project management. The document outlines various metric types, including size-based metrics using lines of code or function points, quality metrics, and metrics for defect removal efficiency. It emphasizes integrating metrics into the software process through establishing a baseline, collecting data, and providing feedback to facilitate continuous process improvement.
Similar to Artificial Intelligence: Case-based & Model-based Reasoning (20)
Chapter 4.ppt mizan Tepi university student population
Chapter 4.ppt mizan Tepi university student population
System Simulation and Modelling with types and Event Scheduling
System Simulation and Modelling with types and Event Scheduling
Parallel Rule Generation For Efficient Classification System
Parallel Rule Generation For Efficient Classification System
Experimental Design for Distributed Machine Learning with Myles Baker
Experimental Design for Distributed Machine Learning with Myles Baker
Mining Performance Regression Testing Repositories for Automated Performance ...
Mining Performance Regression Testing Repositories for Automated Performance ...
The Benefits of using Systems Thinking in Project Management
The Benefits of using Systems Thinking in Project Management
More from The Integral Worm
Artificial Intelligence: Artificial Neural Networks
This document summarizes artificial neural networks (ANN), which were inspired by biological neural networks in the human brain. ANNs consist of interconnected computational units that emulate neurons and pass signals to other units through connections with variable weights. ANNs are arranged in layers and learn by modifying the weights between units based on input and output data to minimize error. Common ANN algorithms include backpropagation for supervised learning to predict outputs from inputs.
Artificial Intelligence: Data Mining
This document discusses data mining, including its motivations, process, techniques, business applications, and current issues. Data mining involves extracting knowledge from large amounts of data through techniques like classification, regression, and clustering. It has various business applications in marketing, finance, and insurance. Selecting the right application requires considering both technical factors like data quality and non-technical factors like potential benefits and management support.
Artificial Intelligence: Agent Technology
This document discusses and compares expert systems and agents. It defines expert systems as rule-based or case-based knowledge systems that are separate from their inference engines. While expert systems can be readily reused, they have limitations like being brittle, isolated, and static. Agents overcome these limitations by being autonomous, adaptive systems that can communicate and learn from each other. The document defines agents and describes different types including deliberative and reactive agents, as well as interface, information, mobile, and stationary agents.
Artificial Intelligence: Knowledge Acquisition
This document discusses different types of knowledge and methods for knowledge acquisition. It describes declarative and procedural knowledge, as well as the knowledge acquisition paradox where experts have difficulty verbalizing their knowledge. Various knowledge acquisition methods are outlined, including observation, problem discussion, and protocol analysis. Knowledge representation techniques like rules, semantic networks, frames, and predicate logic are also introduced.
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
Artificial Intelligence: Knowledge Engineering
The document discusses the requirements and components of expert systems. It notes that expert systems must have a narrow problem area requiring significant human expertise. They must perform at an expert level and be able to explain their reasoning. Structurally, they require a knowledge base containing domain expertise separated from inference procedures, as well as a working memory, inference engine, explanation facility, and user interface. The knowledge base stores facts and rules, the inference engine applies rules to derive new facts, and working memory stores known facts.
Artificial Intelligence: Expert Systems Components
This presentation is an introduction to artificial intelligence: expert systems components. Topics covered are the following: defining artificial intelligence; expert systems key terms; expert systems requirements; expert systems components; and selecting appropriate problems for expert systems.
Best Practices for Effective Written Correspondence
This presentation discusses best practices for composing effective written correspondence. Topics discussed I this presentation are as follows: preparing correspondence; choosing technical level; level of technical expertise; elements of style; standard parts of a letter; structure of business letters, letter patterns--space pattern, changing perspective pattern; explanations, analyses, evaluations, personal disclosures, adjustments, causal pattern, proportional pattern; writing effective memos, language for correspondence, you attitude --reader awareness; memo format and parts; memo structure, memo--succeeding paragraphs, memo—conclusion, memo--good news/bad news; and effective email messages.
Ethical Considerations in Technical Writing and the Workplace
Technical writers face important ethical issues in their work, as the documents they produce can significantly impact users and society. They must consider how to accurately present information to avoid harming others through misunderstanding or misuse, while also serving the interests of their employers. Striking the right balance requires technical writers to understand and navigate tensions between their duties to different stakeholders.
Best Practices for Creating Definitions in Technical Writing and Editing
This presentation describes best practices for creating and documenting definitions in technical writing and editing. Topics covered are the following: effective definitions, multiple meanings, defining technical nomenclature, defining symbols, formal definitions, and informal definitions, and placement of definitions.
Best Practices for Using Visuals in Technical Writing
This presentation outlines industry best practices for using visuals in technical writing. Appropriate visuals should be used only as needed to aid the user in understanding of the task at hand. Visuals are more specific, reduce processing time, and are more quickly understood than text. Visuals should make sense standing alone but also may be used to illustrate, explain, demonstrate, verify, and/or provide support. Use a verbal table to show problems with analyses and solutions for a trouble-shooting section. Visuals are easier to understand than prose in paragraphs. “A picture is worth a 1,000 words…” or at least a couple of hundred. Make sure the number of visuals is appropriate to the audience need and subject matter at hand. Adapt visuals to audience level without dumbing down the subject matter. If there are more than 5 independent visuals within the document, create a List of Figures/Tables. Place visuals as close as possible following (after) the text reference. Surround the visual with white space to separate from text of document. Avoid overuse of decorative color and too much color this only creates confusion for the user in meaning-making. A good example of the use of color are geographical maps and metropolitan train maps.
Best Practices and Guidelines for Collaboration in Workplace Communications
This presentation outlines industry best practices for collaboration in workplace communications. The discussion includes the following: why we collaborate, benefits of collaboration, potential problems in collaboration, how to effectively collaborate, types of collaboration, and how to ensure successful collaboration.
Best Practices and Guidelines for Writing Analytical Reports
This document discusses different types of reports and provides guidance on writing reports. It covers the key differences between informal and formal reports, including appropriate structure, level of formality, and necessary sections. For both types of reports, audience analysis is important to determine the appropriate level of detail and documentation. The document also outlines the typical sections for various report types, such as task reports, progress reports, and analytical reports. It provides guidance on defining the problem, suggesting and evaluating solutions, and organizing the report.
Best Practices for Writing and Editing User/Instruction Manuals
This document provides best practices for creating effective instruction manuals. It discusses analyzing the task and intended audience to understand purpose and user needs. Key sections of a manual are outlined such as introduction, overview, instructions, troubleshooting. Guidelines are given for visual elements, organization, writing style, and ensuring usability through design considerations like chunking, labeling, and parallel structure. The document emphasizes performing a full audience analysis, using precise language and formatting, and including only necessary and accurate details, warnings, and visual aids.
The Good, the bad, and the ugly of Thin Client/Server Computing
This presentation discusses the features, benefits, advantages and disadvantages of using Thin Client/Server technology as an IT Security strategy. The full discussion of Thin Client/Server technology as a security solution may be found at http://www.theintegralworm.com/security.html .
Legal Aspects of Information Systems: State of Maryland vs. CyberSmoke.
This presentation is part of a collaborative study in UMBC’s Legal Aspects of Information Systems providing a summary of our findings. The full study may be found at http://www.theintegralworm.com/legalaspects.html CyberSmoke is a fictitious company that sells tobacco products on the Internet. The study describes CyberSmoke’s business practices and the case brought against it by the State of Maryland outlining several illegal and unethical business practices. The study concludes with the court’s review of the case and its decision.
Rolling credits/Presenters are as follows: Christopher Paul, Nancy Nguyen, Gennadiy Vekker, Tim Baldwin, Christopher Brune
The Test Subject Simulation of the "Cyberpeople Jack Implant" Artifact
The Test Subject Simulation of the “Cyberpeople Jack Implant” slide show is NOT meant to be understood as a “Stand Alone” document. This slide show is a fragment of a larger communication, a multi-modal argument, and cannot be understood unless the user reads through the entire Theory of Communication and Technology Communicative Objective # 1: The “Cyberpeople” Re-patent, which may be found at http://www.theintegralworm.com/patentreadexplaination.html . This link provides an explanation of how to read and proceed though the communication.
The Test Subject Simulation of the “Cyberpeople Jack Implant” is a segment of the URCAD 2007, 11th Undergraduate Research and Creative Achievement Day at UMBC, my presentation of a Three-dimensional Multi-Modal/Multi-Media Argument, “The Cyberpeople Jack Implant: A Study of Remediation, Ethics, and Technology.” This slide show played in an infinite loop at a rate of one slide per second on a laptop during the presentation.
UMBC IFSM438 Project Management Group Presentation
This document outlines a project to develop a plan to refurbish or add onto the existing playground at the UMBC Child Care Center. It introduces the project team members and describes the project scope, approach, communication plan, stakeholders analysis, sponsor interview, SWOT analysis, work breakdown structure, Gantt chart, and cost/benefits analysis of two design alternatives - an all-in-one playground kit ("Project A") and replacing/refurbishing the existing equipment ("Project B"). The team's task is to select a design and present a proposal to the project sponsor by May 3, 2002 within the $2,000 budget.
Best communication design practices when using “Shape Tools” for visual prese...
This presentation is a summary of L. L. Lohr's Creating Graphics for Learning Performance: Lessons in Visual Literacy, Chapter 5 -- "Shape Tools." This slide show outlines best practices in visual literacy illustrating the use of shapes in presentations for delivering communications. The show discusses when to use particular shapes, where the shapes should be used, how the shapes should be used, and why particular shapes are used.
More from The Integral Worm (19)
Artificial Intelligence: Artificial Neural Networks
Artificial Intelligence: Artificial Neural Networks
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...
Artificial Intelligence: Expert Systems Components
Artificial Intelligence: Expert Systems Components
Best Practices for Effective Written Correspondence
Best Practices for Effective Written Correspondence
Ethical Considerations in Technical Writing and the Workplace
Ethical Considerations in Technical Writing and the Workplace
Best Practices for Creating Definitions in Technical Writing and Editing
Best Practices for Creating Definitions in Technical Writing and Editing
Best Practices for Using Visuals in Technical Writing
Best Practices for Using Visuals in Technical Writing
Best Practices and Guidelines for Collaboration in Workplace Communications
Best Practices and Guidelines for Collaboration in Workplace Communications
Best Practices and Guidelines for Writing Analytical Reports
Best Practices and Guidelines for Writing Analytical Reports
Best Practices for Writing and Editing User/Instruction Manuals
Best Practices for Writing and Editing User/Instruction Manuals
The Good, the bad, and the ugly of Thin Client/Server Computing
The Good, the bad, and the ugly of Thin Client/Server Computing
Legal Aspects of Information Systems: State of Maryland vs. CyberSmoke.
Legal Aspects of Information Systems: State of Maryland vs. CyberSmoke.
The Test Subject Simulation of the "Cyberpeople Jack Implant" Artifact
The Test Subject Simulation of the "Cyberpeople Jack Implant" Artifact
UMBC IFSM438 Project Management Group Presentation
UMBC IFSM438 Project Management Group Presentation
Best communication design practices when using “Shape Tools” for visual prese...
Best communication design practices when using “Shape Tools” for visual prese...
Recently uploaded
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GridMate - End to end testing is a critical piece to ensure quality and avoid...
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsLF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Recently uploaded (20)
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Artificial Intelligence: Case-based & Model-based Reasoning
- 1. Case-based Reasoning (CBR) • Collection of a set of cases – Store a case in the Case Base • CBR is based on human information processing (HIP) model in some problem areas – Thinking about how human processes information – Try to remember previous case/recall similar cases & modify to fit a new situation • Examples: Law, diagnosis, strategic planning – CEO: How should we modify last year’s plan? • Human experts depend heavily on past experiences when solving new problems
- 2. Case-based Reasoning • A CBR emulates this HIP model and maintains a historical case • It retrieves cases relevant to the present problem situation from the case base and decides on the solution to the current problem on the basis of the outcomes from previous cases
- 3. CBR Components • A case-based ES consists of – a case base – a retriever – an adapter – a refiner – an executer – an evaluator – (See diagram on next slide)
- 4. CBR Components User Interface Request Relevant Adapter Evaluator Retriever Case Base New Cases (w/ Results) Executer Refiner Prior Cases Prior Result Draft Solution Refined Solution Solution Performance Cases w/o Results
- 5. Case-based Parts • A case base functions as a repository of prior cases • The cases are indexed ( a key as with a database) so that they can be quickly recalled when necessary • A case contains the general descriptions of old problems • In Knowledge Base: Set of Rules • In Case Base: Set of Cases – Creates extra difficulty in retrieving
- 6. Case-base Parts - The Retriever • When a new problem is entered into a case based system, a retriever decides on the features similar to the stored cases • Retrieval is done by using features of the new cases as indexes into the case base
- 7. Case-based Parts - The Adapter • An adapter examines the differences between these cases and the current problem • It then applies rules to modify the old solution to fit the new problem
- 8. Case-based Parts - The Refiner • A refiner critiques the adapted solution against prior outcomes • One way to do this is to compare it to similar solutions of prior cases • If a known failure exists for a derived solution, the system then decides whether the similarities is sufficient to suspect that the new solution will fail
- 9. Case-based Parts - The Executor • Once a solution is critiqued, an executer applies the refined solution to the current problem
- 10. Case-based Parts - The Evaluator • If the results are as expected, no further analysis is made, and the cases and its solution is stored for use in future problem solving • If not, the solution is repaired
- 11. Model-based Reasoning • A model-based system is based on a model of the structure and behavior of the device that the system is designed to simulate • Used for well structured problems – Not for stock pricing/modeling, not well structured – Engineering Problems • Ex: Diagnosing hardware or a machine • Ex: Automobile diagnostics • Based on written documentation • The problem is extracting knowledge
- 12. Model-based Reasoning • Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do) • The difference between them is called a discrepancy, indicating a defect • Then a process is initiated to diagnose the nature and location of the defect • Could be a mathematical equation
- 13. Model-based Reasoning Actual Device Model Predicted Behavior Observed Behavior Discrepancy
- 14. Model-based Reasoning • Correct Operation: • Assume we have a model-based system built to diagnose the following simple device with 3 multipliers and 2 adders • Once the logic is developed, executes quickly A = 3 B = 3 C = 2 D = 2 A = 3 Mult - 1 Mult - 2 Mult -3 Add - 3 Add - 3 F = 12 G = 12 3 2 3 2 2 3 6 6 6 6 6 6 6 12 12
- 15. Model-based Reasoning • Incorrect Operation: – Diagnostics A = 3 B = 3 C = 2 D = 2 A = 3 Mult - 1 Mult - 2 Mult -3 Add - 3 Add - 3 F = 10 G = 12 3 2 3 2 2 3 6 6 6 6 6 6 6 10 12 6 6 6 Culprit of problem? No - 6 comes out Culprit of problem? No - 6 comes out Culprit of problem? No - 6 comes out Culprit of problem? Yes - 10 comes out, problem with Carry Bit Predicate Logic can be used here