This document summarizes Pat Langley's research on learning hierarchical task networks (HTNs) from problem solving. The key points are:
1) It presents an approach to interleaving HTN execution with classical planning when impasses are encountered, allowing HTNs to be learned from problem solving traces.
2) It introduces a specialized class of HTNs that can be executed reactively but in a goal-directed manner.
3) It describes a method for learning new HTN methods by analyzing successful problem solving traces to determine the hierarchical structure, heads, and conditions of learned clauses.
An Argumentation-based Approach for Explaining Goal Selection in Intelligent ...Henrique Jasinski
This document proposes an argumentation-based approach for explaining goal selection in intelligent agents. It discusses how agents can generate explanations for the goals they pursue or are pursuing by using an argumentation framework. The approach was presented in a previous work and generates instrumental arguments from goals and plans. It then identifies conflicts between arguments, applies semantics to determine pursued goals, and generates explanatory arguments. Finally, it constructs natural language explanations using explanation schemes derived from the explanatory arguments. The approach aims to improve the quality and completeness of explanations agents can provide for their goal selection.
Acceptability Paradigms in Abstract Argumentation FrameworksCarlo Taticchi
An overview of the three main paradigms for deriving semantics through acceptability of arguments in AFS: extension-based, labelling-based and ranking-based.
In this thesis, we study the dynamics of AFs from multiple perspectives with the purpose of better understanding how dynamic (and concurrent) processes can be handled in the context of argumentation. In this sense, we provide theoretical results, algorithms and tools which can be useful in many dynamic aspects of argumentation. Before arriving to define our concurrent language, we set the theoretical results we need to work with dynamics aspect of argumentation.
Cluster basics: Introduction to the Cluster ConceptTCI Network
This document provides an introduction to clusters and cluster-based competitiveness initiatives. It defines what a cluster is, including geographic concentrations of interconnected companies and institutions in a particular field. It discusses how cluster-based initiatives have emerged around the world to increase competitiveness. The basics of cluster-based initiatives are explained, including how they focus on business segments rather than statistical sectors and use clusters as a place to discuss strategy and drive related actions. Benefits of clusters for enterprises are outlined.
Cluster basics: Cluster Development in Twelve StepsTCI Network
This document outlines a 12 step process for cluster development. It begins with identifying and prioritizing industry clusters in a local economy. The next steps include initial cluster analysis, developing a shared understanding of competitiveness, and establishing a vision for the preferred future state. Later steps focus on short and long-term strategic agendas through benchmarking, linking clusters nationally and internationally, and periodic reviews to upgrade competitiveness. The process aims to drive economic development through industry collaboration rather than isolated projects. It emphasizes the importance of building relationships and finding common ground to achieve real transformation over time.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it's not a replacement for humans – and won't be anytime soon
Based on the provided data, I do not have enough information to make an accurate diagnosis. Some key data points that would be helpful include:
- Details on any symptoms (e.g. chest pain type if present)
- ECG or imaging test results
- Family history of heart disease
- Whether the patient smokes
- Details on lifestyle factors like diet, exercise and stress levels
Without more clinical context, I cannot determine if this patient shows signs of heart disease or what the likely diagnosis may be. Please provide additional medical history if you would like me to try assessing this case.
An Argumentation-based Approach for Explaining Goal Selection in Intelligent ...Henrique Jasinski
This document proposes an argumentation-based approach for explaining goal selection in intelligent agents. It discusses how agents can generate explanations for the goals they pursue or are pursuing by using an argumentation framework. The approach was presented in a previous work and generates instrumental arguments from goals and plans. It then identifies conflicts between arguments, applies semantics to determine pursued goals, and generates explanatory arguments. Finally, it constructs natural language explanations using explanation schemes derived from the explanatory arguments. The approach aims to improve the quality and completeness of explanations agents can provide for their goal selection.
Acceptability Paradigms in Abstract Argumentation FrameworksCarlo Taticchi
An overview of the three main paradigms for deriving semantics through acceptability of arguments in AFS: extension-based, labelling-based and ranking-based.
In this thesis, we study the dynamics of AFs from multiple perspectives with the purpose of better understanding how dynamic (and concurrent) processes can be handled in the context of argumentation. In this sense, we provide theoretical results, algorithms and tools which can be useful in many dynamic aspects of argumentation. Before arriving to define our concurrent language, we set the theoretical results we need to work with dynamics aspect of argumentation.
Cluster basics: Introduction to the Cluster ConceptTCI Network
This document provides an introduction to clusters and cluster-based competitiveness initiatives. It defines what a cluster is, including geographic concentrations of interconnected companies and institutions in a particular field. It discusses how cluster-based initiatives have emerged around the world to increase competitiveness. The basics of cluster-based initiatives are explained, including how they focus on business segments rather than statistical sectors and use clusters as a place to discuss strategy and drive related actions. Benefits of clusters for enterprises are outlined.
Cluster basics: Cluster Development in Twelve StepsTCI Network
This document outlines a 12 step process for cluster development. It begins with identifying and prioritizing industry clusters in a local economy. The next steps include initial cluster analysis, developing a shared understanding of competitiveness, and establishing a vision for the preferred future state. Later steps focus on short and long-term strategic agendas through benchmarking, linking clusters nationally and internationally, and periodic reviews to upgrade competitiveness. The process aims to drive economic development through industry collaboration rather than isolated projects. It emphasizes the importance of building relationships and finding common ground to achieve real transformation over time.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it's not a replacement for humans – and won't be anytime soon
Based on the provided data, I do not have enough information to make an accurate diagnosis. Some key data points that would be helpful include:
- Details on any symptoms (e.g. chest pain type if present)
- ECG or imaging test results
- Family history of heart disease
- Whether the patient smokes
- Details on lifestyle factors like diet, exercise and stress levels
Without more clinical context, I cannot determine if this patient shows signs of heart disease or what the likely diagnosis may be. Please provide additional medical history if you would like me to try assessing this case.
This document summarizes key concepts in artificial intelligence planning and logic. It discusses representations like atomic, factored, and structured states. Planning approaches include state-space search, planning graphs, and situation calculus. Factored representations allow more flexible and hierarchical plans using relations between state variables. Planning graphs efficiently represent possible plan states and actions to derive heuristic estimates and extract plans.
This document summarizes an introduction to artificial intelligence planning and logic. It discusses different types of planning problems and representations including classical planning with STRIPS, planning with factored states, partial observability, and extensions like planning graphs and situation calculus. The document also provides an overview of the GRAPHPLAN algorithm for solving planning problems using planning graph representations.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
The document discusses backward search planning and plan-space planning techniques. Backward search starts at the goal and works backwards to find a plan, and can have a lower branching factor than forward search. Plan-space planning formulates planning as a constraint satisfaction problem to produce partially ordered plans with more flexibility. It works by iteratively refining a partial plan to resolve flaws such as open goals or threats between causal links, until a solution plan with no flaws is found.
To prevent somebody from being their own sister, the rule for sister_of/2 can be modified to add the constraint that X and Y must be different individuals:
sister_of(X, Y) :-
female(X),
X \= Y,
parents(X, M, F),
parents(Y, M, F).
This adds the check X \= Y to ensure X and Y are not the same person before concluding they are sisters.
This document discusses negation in Prolog. It begins by explaining how to use the not operator to avoid wrong answers when replacing cuts, and gives an example using not for inequality checks. It then discusses negation as failure in Prolog, and how the closed world assumption can lead to surprises. It provides examples and advice on the safe use of negation, and explains that double negation does not cancel out in Prolog. It also introduces the fail predicate and shows how it can be used to define falsehoods and represent exceptions. Throughout it traces examples to illustrate Prolog's behavior with negation.
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Köhler, Sven, Bertram Ludäscher, and Yannis Smaragdakis. 2012. “Declarative Datalog Debugging for Mere Mortals.” In Datalog in Academia and Industry, edited by Pablo Barceló and Reinhard Pichler, 111–22. Lecture Notes in Computer Science 7494. Springer Berlin Heidelberg. doi:10.1007/978-3-642-32925-8_12.
Abstract. Tracing why a “faulty” fact A is in the model M = P(I) of program P on input I quickly gets tedious, even for small examples. We propose a simple method for debugging and “logically profiling” P by generating a provenance-enriched rewriting P̂, which records rule firings according to the logical semantics. The resulting provenance graph can be easily queried and analyzed using a set of predefined and ad-hoc queries. We have prototypically implemented our approach for two different Datalog engines (DLV and LogicBlox), demonstrating the simplicity, effectiveness, and system-independent nature of our method.
The document discusses uninformed search techniques. It provides examples of representing problems as states and operators that transform states. This includes problems like the water jug problem, 8-puzzle, and 8-queens. It then describes common uninformed search algorithms like breadth-first search, depth-first search, iterative deepening, and uniform cost search. It analyzes the properties of these algorithms like completeness, time complexity, space complexity, and optimality.
Abstract : For many years, Machine Learning has focused on a key issue: the design of input features to solve prediction tasks. In this presentation, we show that many learning tasks from structured output prediction to zero-shot learning can benefit from an appropriate design of output features, broadening the scope of regression. As an illustration, I will briefly review different examples and recent results obtained in my team.
This document provides information about an artificial intelligence course at Sanjivani College of Engineering, including the course objectives, an overview of planning in AI, types of planning strategies like forward and backward state space planning, and an example of the block world planning problem. It also discusses logical representations of planning problems using first-order logic, the STRIPS planning framework, and an algorithm for goal stack planning.
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
The document provides examples of code translations between OCaml and Java, asking the user to complete code snippets in Java based on OCaml examples. It tests knowledge of data structures, classes, interfaces, inheritance, exceptions, recursion, and the object-oriented design process. The user is prompted to attempt answering questions without looking at the answers first to maximize learning.
Scala has many symbols, operators, and syntax elements that can be confusing for beginners. The underscore character in particular takes on many different meanings depending on context. Type erasure in Scala means that types are not always available at runtime. Initialization order of vals follows a defined rule where traits are initialized before subclasses. Having a deep understanding of Scala's semantics is important to explain unexpected behavior.
The document contains 35 multiple choice questions about C language concepts such as data types, operators, functions, arrays, pointers, structures, etc. Each question is followed by 4 possible answer choices with one correct answer. The questions cover basic to advanced C programming topics including arithmetic expressions, control structures, memory allocation, recursion, data structures and more.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
This document contains 20 multiple choice questions about pointers, arrays, structures and other C programming concepts. The questions cover topics like pointers and pointer arithmetic, arrays, structures, strings, data types and more. Multiple choice options are provided for each question to test the reader's knowledge of these fundamental C programming concepts.
The document discusses classical AI planning and different planning approaches. It introduces state-space planning which searches for a sequence of state transformations, and plan-space planning which searches for a plan satisfying certain conditions. It also discusses hierarchical planning which decomposes tasks into simpler subtasks, and universal classical planning which uses different refinement techniques including state-space and plan-space refinements. Classical planning makes simplifying assumptions but its principles can still be applied to games with some workarounds.
This document discusses work study techniques used to analyze human work and improve efficiency. It defines work study, method study, and work measurement. Method study examines work processes to develop more effective methods using a six-step approach. Work measurement techniques include time study, activity sampling, and predetermined time systems to establish standard times for tasks. The goals of work study are to optimize the use of resources, increase productivity, reduce costs, and improve working conditions and quality.
Work study is a technique used to systematically analyze work processes to improve efficiency. It involves observing work, documenting the current process, analyzing it for improvements, developing a more efficient method, measuring the new method, and implementing it as the standard. The key founders who developed techniques in this area include Frederick Taylor with time motion studies, Frank and Lillian Gilbreth with motion studies, and Henry Gantt with task scheduling. Work study specialists must gain cooperation from supervisors and workers, and consider the human factors, to successfully conduct a study and implement new standards.
This document summarizes key concepts in artificial intelligence planning and logic. It discusses representations like atomic, factored, and structured states. Planning approaches include state-space search, planning graphs, and situation calculus. Factored representations allow more flexible and hierarchical plans using relations between state variables. Planning graphs efficiently represent possible plan states and actions to derive heuristic estimates and extract plans.
This document summarizes an introduction to artificial intelligence planning and logic. It discusses different types of planning problems and representations including classical planning with STRIPS, planning with factored states, partial observability, and extensions like planning graphs and situation calculus. The document also provides an overview of the GRAPHPLAN algorithm for solving planning problems using planning graph representations.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
The document discusses backward search planning and plan-space planning techniques. Backward search starts at the goal and works backwards to find a plan, and can have a lower branching factor than forward search. Plan-space planning formulates planning as a constraint satisfaction problem to produce partially ordered plans with more flexibility. It works by iteratively refining a partial plan to resolve flaws such as open goals or threats between causal links, until a solution plan with no flaws is found.
To prevent somebody from being their own sister, the rule for sister_of/2 can be modified to add the constraint that X and Y must be different individuals:
sister_of(X, Y) :-
female(X),
X \= Y,
parents(X, M, F),
parents(Y, M, F).
This adds the check X \= Y to ensure X and Y are not the same person before concluding they are sisters.
This document discusses negation in Prolog. It begins by explaining how to use the not operator to avoid wrong answers when replacing cuts, and gives an example using not for inequality checks. It then discusses negation as failure in Prolog, and how the closed world assumption can lead to surprises. It provides examples and advice on the safe use of negation, and explains that double negation does not cancel out in Prolog. It also introduces the fail predicate and shows how it can be used to define falsehoods and represent exceptions. Throughout it traces examples to illustrate Prolog's behavior with negation.
Intro to AI STRIPS Planning & Applications in Video-games Lecture3-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Köhler, Sven, Bertram Ludäscher, and Yannis Smaragdakis. 2012. “Declarative Datalog Debugging for Mere Mortals.” In Datalog in Academia and Industry, edited by Pablo Barceló and Reinhard Pichler, 111–22. Lecture Notes in Computer Science 7494. Springer Berlin Heidelberg. doi:10.1007/978-3-642-32925-8_12.
Abstract. Tracing why a “faulty” fact A is in the model M = P(I) of program P on input I quickly gets tedious, even for small examples. We propose a simple method for debugging and “logically profiling” P by generating a provenance-enriched rewriting P̂, which records rule firings according to the logical semantics. The resulting provenance graph can be easily queried and analyzed using a set of predefined and ad-hoc queries. We have prototypically implemented our approach for two different Datalog engines (DLV and LogicBlox), demonstrating the simplicity, effectiveness, and system-independent nature of our method.
The document discusses uninformed search techniques. It provides examples of representing problems as states and operators that transform states. This includes problems like the water jug problem, 8-puzzle, and 8-queens. It then describes common uninformed search algorithms like breadth-first search, depth-first search, iterative deepening, and uniform cost search. It analyzes the properties of these algorithms like completeness, time complexity, space complexity, and optimality.
Abstract : For many years, Machine Learning has focused on a key issue: the design of input features to solve prediction tasks. In this presentation, we show that many learning tasks from structured output prediction to zero-shot learning can benefit from an appropriate design of output features, broadening the scope of regression. As an illustration, I will briefly review different examples and recent results obtained in my team.
This document provides information about an artificial intelligence course at Sanjivani College of Engineering, including the course objectives, an overview of planning in AI, types of planning strategies like forward and backward state space planning, and an example of the block world planning problem. It also discusses logical representations of planning problems using first-order logic, the STRIPS planning framework, and an algorithm for goal stack planning.
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
The document provides examples of code translations between OCaml and Java, asking the user to complete code snippets in Java based on OCaml examples. It tests knowledge of data structures, classes, interfaces, inheritance, exceptions, recursion, and the object-oriented design process. The user is prompted to attempt answering questions without looking at the answers first to maximize learning.
Scala has many symbols, operators, and syntax elements that can be confusing for beginners. The underscore character in particular takes on many different meanings depending on context. Type erasure in Scala means that types are not always available at runtime. Initialization order of vals follows a defined rule where traits are initialized before subclasses. Having a deep understanding of Scala's semantics is important to explain unexpected behavior.
The document contains 35 multiple choice questions about C language concepts such as data types, operators, functions, arrays, pointers, structures, etc. Each question is followed by 4 possible answer choices with one correct answer. The questions cover basic to advanced C programming topics including arithmetic expressions, control structures, memory allocation, recursion, data structures and more.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
This document contains 20 multiple choice questions about pointers, arrays, structures and other C programming concepts. The questions cover topics like pointers and pointer arithmetic, arrays, structures, strings, data types and more. Multiple choice options are provided for each question to test the reader's knowledge of these fundamental C programming concepts.
The document discusses classical AI planning and different planning approaches. It introduces state-space planning which searches for a sequence of state transformations, and plan-space planning which searches for a plan satisfying certain conditions. It also discusses hierarchical planning which decomposes tasks into simpler subtasks, and universal classical planning which uses different refinement techniques including state-space and plan-space refinements. Classical planning makes simplifying assumptions but its principles can still be applied to games with some workarounds.
This document discusses work study techniques used to analyze human work and improve efficiency. It defines work study, method study, and work measurement. Method study examines work processes to develop more effective methods using a six-step approach. Work measurement techniques include time study, activity sampling, and predetermined time systems to establish standard times for tasks. The goals of work study are to optimize the use of resources, increase productivity, reduce costs, and improve working conditions and quality.
Work study is a technique used to systematically analyze work processes to improve efficiency. It involves observing work, documenting the current process, analyzing it for improvements, developing a more efficient method, measuring the new method, and implementing it as the standard. The key founders who developed techniques in this area include Frederick Taylor with time motion studies, Frank and Lillian Gilbreth with motion studies, and Henry Gantt with task scheduling. Work study specialists must gain cooperation from supervisors and workers, and consider the human factors, to successfully conduct a study and implement new standards.
Motion and Time Study are methods used to analyze work processes and determine standard times. Frank and Lillian Gilbreth pioneered Motion Study in the 1880s to analyze body motions. Frederick Taylor developed Time Study in the 1880s to measure task completion times. Modern tools like motion cameras, stopwatches, and software are used to study processes in manufacturing, offices, hospitals and more in order to identify inefficiencies and establish performance standards.
The document discusses principles and approaches for improving work efficiency through motion study and work design, including:
1. Four principles of motion economy to reduce unnecessary movement - reduce motions, perform simultaneously, shorten distances, make motions easier.
2. Ergonomic considerations for tool and workstation layout such as positioning parts and tools for sequential use, gravity feeding, and correct working heights.
3. Methods for analyzing and improving operations including eliminating unnecessary steps, simplifying processes, combining tasks, and rearranging sequences.
The passage describes the birth of Jesus Christ as foretold by prophets. An angel tells Mary she will conceive and give birth to Jesus, the Son of God. Mary visits her relative Elizabeth, who is also miraculously pregnant. Mary gives birth to Jesus in Bethlehem, where he is visited by shepherds and wise men. The passage establishes Jesus as the promised Messiah through fulfillment of prophecies.
The document provides a list of images related to sights and landmarks in Turkey. It includes pictures of mountains, cave houses, mosques, palaces, bridges, museums, universities, and natural areas from various cities across Turkey including Istanbul, Ankara, Konya, Antalya, and Edirne. The images showcase Turkey's rich cultural and architectural heritage as well as natural beauty.
The Society of Concurrent Product Development (SOCPD) aims to promote integrated product development practices. Its mission is to disseminate knowledge of concurrent engineering and further develop its body of knowledge. The document outlines SOCPD's history and evolution since the 1980s, including expanding its mission, objectives, and body of knowledge to keep up with emerging techniques and technologies in concurrent product development.
The document discusses Miramar Automation's industrial automation and control engineering services. It lists hundreds of successful projects in areas like process control, SCADA, telemetry, water handling, and beverage preparation. It also outlines the company's capabilities such as software development, panel/PLC integration, gas detection systems, engineering documentation, and training.
The document is a poem about a mother's unconditional love and sacrifice for her child from birth through adulthood. It describes how the mother was always there to support and celebrate her child at each milestone, from standing up and taking first steps to starting school, getting sick, riding a bicycle, and becoming independent. It reminds the reader to show their love and spend time with their mother while they can, as the mother has given her all through the years and now in old age just desires a bit of her child's love and time in return.
Linearization involves developing a linear approximation of a nonlinear system around an operating point. This allows tools from linear systems theory to be applied to analyze and design controllers for nonlinear systems. Specifically, Taylor's theorem is used to expand the nonlinear functions as a linear combination of deviations from the operating point. The resulting linearized model is only valid locally but provides an approximate way to analyze system behavior if well-controlled near the operating point. Examples show how to derive linearized models for common nonlinear systems like tanks and chemical reactors.
Kblmt B000 Intro Kaizen Based Lean Manufacturingahmad bassiouny
The document introduces Kaizen-based Lean Manufacturing (KBLM) as a way for businesses to increase speed, improve quality, and lower costs compared to traditional Material Requirements Planning (MRP) systems. It discusses eight basics of KBLM, including information integrity, motivational measurement, sequential production, point-of-use logistics, cycle time management, production linearity, resource planning, and customer connectivity. Case studies show companies achieving reductions in costs, inventory levels, and product build times by implementing KBLM.
The document provides seven rules for surviving in a new economy: 1) Accept ambiguity and uncertainty; 2) Hold yourself accountable for outcomes; 3) Become a quick change artist and adapt to changes; 4) Add value through your work; 5) Be a problem solver rather than pointing fingers; 6) Practice continuous improvement through lean processes; 7) Continue learning new skills. The introductory quote emphasizes embracing positive change and progress rather than resisting it or becoming part of negative forces that stand in the way.
A son or daughter's perspective of their father changes dramatically over time, from seeing their father as all-knowing when young, to viewing them as outdated and unreasonable during the teenage years, to later gaining appreciation for all the hardships and sacrifices their father endured to raise them as they become parents themselves.
Ancient Egyptian hieroglyphics originated as a sacred writing system used since around 3000 BC, combining alphabetic, logographic, and ideographic elements. It was used by priests, scribes, and educated citizens in different scripts until being replaced by Coptic in the 4th century AD. The Rosetta Stone, discovered in 1799, had the key to decrypting hieroglyphics by bearing a decree in three scripts: Greek, Demotic, and hieroglyphic. It was deciphered in 1822, unlocking understanding of ancient Egyptian writings.
The document discusses various aspects of job design and work systems, including:
1. Job design involves specifying the content, tasks, responsibilities, and methods associated with a job.
2. Ergonomics and behavioral approaches aim to incorporate human factors and motivate workers through variety, autonomy, and feedback.
3. Traditional efficiency approaches focus on specialization and standardization of tasks from a productivity standpoint.
4. Effective job design balances both human and technical factors to optimize performance and satisfaction.
Organizational behavior (OB) involves studying how individuals and groups function within organizations to accomplish work. Managers play an important role as they interact with others and direct activities to achieve organizational and personal goals. Manager's interpersonal skills are important because views of organizations are changing from seeing them as machines to seeing them as social systems where relationships among individuals are important. OB draws on various behavioral disciplines like psychology, sociology, and anthropology to study individual behavior, group behavior, organizational structure, and their impact on organizational effectiveness and efficiency.
The document summarizes the key aspects of an on-campus work-study program, including eligibility requirements, the roles and responsibilities of various parties, and operational guidelines. It outlines the financial aid application process, how student earnings are subsidized, employment conditions, payroll procedures, and the auditing process to ensure compliance. Departments must submit agreements, post available positions, hire eligible students, and monitor hours worked, while the Work-Study Office reviews placements and provides guidance.
The document discusses various topics related to work study and productivity including:
- Defining work study, productivity, and their importance in analyzing work processes and identifying areas for improvement.
- Key aspects of work study such as method study, work measurement, incentive plans, and examining worker-machine relationships.
- Modeling work processes using tools like flow diagrams and developing standard times.
- The role of work study in broader areas like systems analysis, business process reengineering, and human-computer interface design.
Time and motion studies are methods used to determine the optimal time it takes to complete tasks. They were developed by Frederick Taylor and the Gilbreths to establish fair work standards and eliminate unnecessary motions. While originally used in manufacturing, today time and motion studies can be applied to performance evaluations, planning, problem solving, and cost analysis in various organizations. The objective is to study jobs and determine standard times through observation, task breakdown, and time recording. Allowance factors are added to standard times to account for contingencies. However, studies may not always accurately capture real work conditions due to observer or worker issues.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
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How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
SWOT analysis in the project Keeping the Memory @live.pptx
Classical And Htn Planning
1. Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/ Learning Hierarchical Task Networks from Problem Solving Thanks to Dongkyu Choi, Kirstin Cummings, Seth Rogers, and Daniel Shapiro for contributions to this research, which was funded by Grant HR0011-04-1-0008 from DARPA IPTO and by Grant IIS-0335353 from NSF.
11. Some NonPrimitive Recursive Skills (clear (?C) :percepts ((block ?D) (block ?C)) :start (unstackable ?D ?C) :skills ((unstack ?D ?C))) (clear (?B) :percepts ((block ?C) (block ?B)) :start [(on ?C ?B) (hand-empty)] :skills ((unstackable ?C ?B) (unstack ?C ?B))) (unstackable (?C ?B) :percepts ((block ?B) (block ?C)) :start [(on ?C ?B) (hand-empty)] :skills ((clear ?C) (hand-empty))) (hand-empty ( ) :percepts ((block ?D) (table ?T1)) :start (putdownable ?D ?T1) :skills ((putdown ?D ?T1))) [Expanded for readability] Teleoreactive logic programs are executed in a top-down, left-to-right manner, much as in Prolog but extended over time, with a single path being selected on each time step.
12. Interleaving HTN Execution and Classical Planning Solve(G) Push the goal literal G onto the empty goal stack GS. On each cycle, If the top goal G of the goal stack GS is satisfied, Then pop GS. Else if the goal stack GS does not exceed the depth limit, Let S be the skill instances whose heads unify with G. If any applicable skill paths start from an instance in S, Then select one of these paths and execute it. Else let M be the set of primitive skill instances that have not already failed in which G is an effect. If the set M is nonempty, Then select a skill instance Q from M. Push the start condition C of Q onto goal stack GS. Else if G is a complex concept with the unsatisfied subconcepts H and with satisfied subconcepts F, Then if there is a subconcept I in H that has not yet failed, Then push I onto the goal stack GS. Else pop G from the goal stack GS and store information about failure with G's parent. Else pop G from the goal stack GS. Store information about failure with G's parent. This is traditional means-ends analysis, with three exceptions: (1) conjunctive goals must be defined concepts; (2) chaining occurs over both skills/operators and concepts/axioms; and (3) selected skills are executed whenever applicable.
13. A Successful Planning Trace (ontable A T) (on B A) (on C B) (hand-empty) (clear C) (unst. C B) (unstack C B) (clear B) (putdown C T) (unst. B A) (unstack B A) (clear A) (holding C) (hand-empty) (holding B) initial state goal A B C B A C
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15. Recording Results for Learning Solve(G) Push the goal literal G onto the empty goal stack GS. On each cycle, If the top goal G of the goal stack GS is satisfied, Then pop GS and let New be Learn(G). If G's parent P involved skill chaining, Then store New as P's first subskill. Else if G's parent P involved concept chaining, Then store New as P's next subskill. Else if the goal stack GS does not exceed the depth limit, Let S be the skill instances whose heads unify with G. If any applicable skill paths start from an instance in S, Then select one of these paths and execute it. Else let M be the set of primitive skill instances that have not already failed in which G is an effect. If the set M is nonempty, Then select a skill instance Q from M, store Q with goal G as its last subskill, Push the start condition C of Q onto goal stack GS , and mark goal G as involving skill chaining. Else if G is a complex concept with the unsatisfied subconcepts H and with satisfied subconcepts F, Then if there is a subconcept I in H that has not yet failed, Then push I onto the goal stack GS, store F with G as its initially true subconcepts, and mark goal G as involving concept chaining. Else pop G from the goal stack GS and store information about failure with G's parent. Else pop G from the goal stack GS. Store information about failure with G's parent. The extended problem solver calls on Learn to construct a new skill clause and stores the information it needs in the goal stack generated during search.
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17. (ontable A T) (on B A) (on C B) (hand-empty) (clear C) (unst. C B) (unstack C B) (clear B) (putdown C T) (unst. B A) (unstack B A) (clear A) (holding C) (hand-empty) (holding B) 1 skill chaining Constructing Skills from a Trace A B C B A C
18. (ontable A T) (on B A) (on C B) (hand-empty) (clear C) (unst. C B) (unstack C B) (clear B) (putdown C T) (unst. B A) (unstack B A) (clear A) (holding C) (hand-empty) (holding B) 1 2 skill chaining Constructing Skills from a Trace A B C B A C
19. (ontable A T) (on B A) (on C B) (hand-empty) (clear C) (unst. C B) (unstack C B) (clear B) (putdown C T) (unst. B A) (unstack B A) (clear A) (holding C) (hand-empty) (holding B) 1 3 2 concept chaining Constructing Skills from a Trace A B C B A C
20. (ontable A T) (on B A) (on C B) (hand-empty) (clear C) (unst. C B) (unstack C B) (clear B) (putdown C T) (unst. B A) (unstack B A) (clear A) (holding C) (hand-empty) (holding B) 1 3 2 4 skill chaining Constructing Skills from a Trace A B C B A C
26. Learning an HTN Method from a Problem Solution Learn(G) If the goal G involves skill chaining, Then let S 1 and S 2 be G's first and second subskills. If subskill S 1 is empty, Then return the literal for clause S 2 . Else create a new skill clause N with head G, with S 1 and S 2 as ordered subskills, and with the same start condition as subskill S 1 . Return the literal for skill clause N. Else if the goal G involves concept chaining, Then let {C k+1 , ..., C n } be G's initially satisfied subconcepts. Let {C 1 , ..., C k } be G's stored subskills. Create a new skill clause N with head G, with {C k+1 , ..., C n } as ordered subskills, and with the conjunction of {C 1 , ..., C k } as start condition. Return the literal for skill clause N.
27. Creating a Clause from Skill Chaining Problem Solution New Method 1 8 3 X Z 2 8 1 2 X Z Y
28. Creating a Clause from Concept Chaining Problem Solution New Method 8 2 1 Y B X C Z D A A D # B 8 3 Z C
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30. An In-City Driving Environment Our focus on learning for reactive control comes from an interest in complex physical domains, such as driving a vehicle in a city. To study this problem, we have developed a realistic simulated environment that can support many different driving tasks.
34. Transfer Effects in the Blocks World On 20-block tasks, there is no difference in solved problems. 20 blocks
35. Transfer Effects in the Blocks World However, there is difference in the effort needed to solve them. 20 blocks
36. FreeCell Solitaire FreeCell is a full-information card game that, in most cases, can be solved by planning; it also has a highly recursive structure.
37. Transfer Effects in FreeCell On 16-card FreeCell tasks, prior training aids solution probability. 16 cards
38. Transfer Effects in FreeCell However, it also lets the system solve problems with less effort. 16 cards
39. Transfer Effects in FreeCell On 20-card tasks, the benefits of prior training are much stronger. 20 cards
40. Transfer Effects in FreeCell However, it also lets the system solve problems with less effort. 20 cards
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44. The I CARUS Architecture Long-Term Conceptual Memory Long-Term Skill Memory Short-Term Conceptual Memory Goal/Skill Stack Categorization and Inference Skill Execution Perception Environment Perceptual Buffer Problem Solving Skill Learning Motor Buffer Skill Retrieval
45. Hierarchical Structure of Long-Term Memory concepts skills Each concept is defined in terms of other concepts and/or percepts. Each skill is defined in terms of other skills, concepts, and percepts. I CARUS organizes both concepts and skills in a hierarchical manner.
46. Interleaved Nature of Long-Term Memory For example, the skill highlighted here refers directly to the highlighted concepts. I CARUS interleaves its long-term memories for concepts and skills. concepts skills
47. Recognizing Concepts and Selecting Skills concepts skills Concepts are matched bottom up, starting from percepts. Skill paths are matched top down, starting from intentions. I CARUS matches patterns to recognize concepts and select skills.