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.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
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.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
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.
Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Case-based & Model-based ReasoningThe Integral Worm
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.
In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases
may not appear to contain valuable relational information but practically such a relation exists.
The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
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.
Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Case-based & Model-based ReasoningThe Integral Worm
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.
In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases
may not appear to contain valuable relational information but practically such a relation exists.
The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data
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.
In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in
databases at discrete intervals. The data contained in these databases may not appear to contain valuable
relational information but practically such a relation exists. The large number of process parameter values
are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for
the expert system. With the changes in parameters there is a quite high possibility to form new rules using
the dynamics of the process itself. We present an efficient algorithm that generates all significant rules
based on the real data. The association based algorithms were compared and the best suited algorithm for
this process application was selected. The application for the Learning system is studied in a Power Plant
domain. The SCADA interface was developed to acquire online plant data.
Definition, architecture, general applications, and energy management specified application of expert systems - Class presentation - University of Tabriz 2019
New folderIMAG2318.jpgNew folderIMAG2319.jpgNew folder.docxhenrymartin15260
New folder/IMAG2318.jpg
New folder/IMAG2319.jpg
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Task 1.
1. Use case is a type of tool that is used for analyzing systems in order to identify, organize and clarify systems and their requirements. A use case diagram will thus be defined as a graphical presentation of the elements of a system and how these elements interact in performing the required objectives.
Use case has two types of actors:
Primary actors. These are the ones that the system shall provide services to. They include customers.
Secondary actors are those that manage the system in providing services to the customers, and they include system administrators.
2. <<include>> relationship is used in extraction of use case duplicated use case fragments in multiple use cases. An example is where a user accesses the ATM. The first step is pin, which then grants them access to main menu.
<<extend>> relationship applies where a conditional step is to be added to another use case step that is first class, and is not necessarily a step requirement. An example is when a user in an ATM wants to monitor their accounts. Once the main menu is provided, he or she would be able to monitor the balance, and another option of withdrawing would be an extension or addition to the base use class.
3. Encapsulation is the process of combining data and functions of a program into one component. It is used in protecting codes and data from being accessed randomly by other codes that are defined outside the class. An example in java is where getters and setters are used by the public methods in order to access class fields from outside the java.
Information hiding is the process of differentiating design functions and decisions in a computer program which are vulnerable to change, from modification by other programs. An example is where a programmer decides to create an application for managing a database. The programmer retains the information to modify the program while only releasing the part of the program used to access basic database functions to the public.
Polymorphism refers to the use of one interface to access various entities of different types. An example of polymorphism is where various data types are defined for a particular function, and the computation or data modification done using the best data type method among those defined.
Data abstraction is a methodology used in defining the methods of interaction with the system, starting from easiest tending to the most complex interaction method, with the very difficult ones suppressed. An example is where a programmer inputs data for coding, all the data input is basically plain, and the complex part of data representation by the code is not portrayed on the screen for the programmer.
4. Difference between USDP and Waterfall life Cycles.
In USDP, the stages followed from analysis to testing, are conducted in iterative and concurrent manner while in Waterfall, these processes are done in sequ.
Decision Making and Autonomic ComputingIOSR Journals
Abstract: Autonomic Computing refers to the self-managing characteristics of distributed computing
resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users.
An autonomic system makes decisions on its own, using high-level policies; it will constantly check and
optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an
autonomic computing framework might be seen composed by autonomic components interacting with each
other.
An Autonomic Computing can be modeled in terms of two main control loops (local and global) with
sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting
policies based on self- and environment awareness.
The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning
while keeping the system's complexity invisible to the user.
General Terms: Autonomic systems, Self-configuration, Self-healing, Self-optimization, Self-protection.
Keywords: Know itself, reconfigure, recover from extraordinary events, expert in self-protection,
This model is one of the most used mode in the industry to align the Business with IT.
This helps to have an strategic edge over the competitor and see how and what function are affecting each other. Along with knowing that which is the core function that is driving the business.
One of the most successful entrepreneur of India.
Owner of CCD which a lot famous among teenagers for its awesome coffee.
Favorite hangout place for most of the teenagers with lovely ambiance and services.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2. An Expert system is a computer system that emulates the
decision-making ability of a human expert
It is divided into two parts,
Fixed, Independent : The Inference Engine,
Variable: The Knowledge Base
Engine reasons about the
knowledge base like a human.
3. Computer program that tries to derive answers from
a knowledge base.
Brain of Expert System
Inference commonly proceeds by
Forward chaining
Backward chaining
4. ﴾ Grandfather (Tom -Marry)
﴾ Father (Tom -Jack)
﴾ Father (Jack -Mary)
Here there are two facts
₪ Tom is the father of Jack
₪ Jack is the father of Mary
5. An interpreter
The interpreter executes the chosen agenda items by applying
the corresponding base rules.
A scheduler
The scheduler maintains control over the agenda by estimating
the effects of applying inference rules in light of item priorities
or other criteria on the agenda.
A consistency enforcer
The consistency enforcer attempts to maintain a consistent
representation of the emerging solution.
6. Conflict resolution
If there are activations then select the one with the highest
priority else done
Act Sequentially
Perform the actions.
Update the working memory.
Remove the fired activations.
7. Match
Update the agenda - Checking if there are activations if their
LHS is no longer satisfied.
Check for halt - Two commands tell that action is over.
Break
Halt
9. Takes rule and if its conditions are true adds
its conclusion to working memory until no more rules can
be applied
If the conditions of the rule if A and B then C are true
then C is added to working memory.
In forward chaining the system simply test the rules in the
order that occurs therefore rule order is important.
10. The backward chaining inference engines tries to prove a
goal by establishing the truth of its conditions
The rule if A and B then C the backward chaining engine
will try to prove C by first proving A and then proving B.
Proving these conditions to be true may well invoke
further calls to the engine and so on.
11. It is a computer program to solve complex problems.
Reasons
Uses knowledge
Knowledge is acquired represented using various
knowledge representation
Techniques
Rules,
Frames and
Scripts.
User Inference Knowledge
Interface engine base
12.
13. There are specialized systems for knowledge workers
To help them to create new knowledge
To ensure that this knowledge is properly integrated into the business
Critical Key roles of knowledge workers
Keeping the current knowledge
Serving as internal consultants regarding the areas of their knowledge
Acting as change agents
Knowledge work systems require strong links
To external knowledge bases in addition to specialized hardware and software.
14. CAD/CAM systems:
Computer-aided design (CAD) and Computer-aided
manufacturing (CAM) systems automate
The creation and
Revision of designs,
using computers and sophisticated graphics software.
They provide
Engineers,
Designers, and
Factory managers
with precise manufacturing control over industrial design and
manufacturing
15. Virtual reality systems:
These use interactive graphics software to
Aid drug designers,
Architects,
Engineers, and
Medical workers
by presenting precise, three-dimensional simulations of objects.
16. Investment workstations:
These are high-end PCs used in the financial sector
To analyze trading situations instantaneously and
Facilitate portfolio management.