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Unit 1 ai

  2. 2. Definitions:- Artificial Intelligence (AI) is a branch of Science which deals with helping machines finds solutions to complex problems in a more human-like fashion. (or) Artificial intelligence (AI) is the study and design of machines or computational methods that can perform tasks that normally require human intelligence. (or) "Artificial intelligence is the ability of a human-made machine (automation) to match or simulate human methods for the deductive and inductive acquisition and application of knowledge and reason ". (or) "Artificial intelligence is the study of how to make computers do things at which at the moment, people are better". (or) "Artificial intelligence is that branch of computer science dealing with symbolic, non-algorithmic methods of problem solving". (or) "Artificial intelligence is part of computer science concerned with designing intelligent computer systems that is systems that exhibit the characteristics we associate with intelligence in human behavior".
  3. 3. Intelligence: Intelligence is the ability to acquire and apply the knowledge. Knowledge is the information acquired through experience. Experience is the knowledge gained through training. Summing the terms, we get artificial intelligence as the “copy of something natural (i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.” Intelligence can be simply defined as a set of properties of the mind. These properties include the ability to plan, solve problems, and in general, reason. A simpler definition could be that intelligence is the ability to make the right decision given a set of inputs and a variety of possible actions.
  4. 4. Intelligence is composed of: •Reasoning •Learning •Problem Solving •Perception •Linguistic Intelligence Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuro-science, artificial psychology and many others.
  5. 5. Need for Artificial Intelligence: •To create expert systems which exhibit intelligent behavior with the capability to learn, demonstrate, explain and advice its users. •Helping machines find solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner. Artificial intelligence is an emerging technology science that studies and develops the theory, technology and application systems for simulating and extending human intelligence, involving disciplines such as psychology, cognitive science, thinking science, information science, systems science and bioscience. The Artificial intelligence is in fact the simulation of the process of data interaction of human thinking, hoping to understand the essence of human intelligence and then produce a smart machine, this intelligent machine can be the same as human thinking to respond and deal with the problem. Artificial intelligence has provided great potential and space for the optimization of electrical engineering, and it will bring about great improvement not only in economic aspect, but also in safety and actual operation control.
  6. 6. Applications of AI: It deals with the various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others. Among the application areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural language processing, Image recognition, Robotics and many others. The subject of AI has been enriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and Engineering. Thus in figure, they have been referred to as the parent disciplines of AI. An at-a-glance look at figure also reveals the subject area of AI and its application areas.
  7. 7. Fig: AI its parent disciplines and application areas.
  8. 8. · Gaming − AI plays important role for machine to think of large number of possible positions based on deep knowledge in strategic games. For example: chess, river crossing, N-queens problems and etc. · Natural Language Processing − Interact with the computer that understands natural language spoken by humans. · Expert Systems − Machine or software provide explanation and advice to the users. · Vision Systems − Systems understand, explain, and describe visual input on the computer. · Speech Recognition − There are some AI based speech recognition systems have ability to hear and express as sentences and understand their meanings while a person talks to it. For example: Siri and Google assistant. · Handwriting Recognition − the handwriting recognition software reads the text written on paper and recognize the shapes of the letters and convert it into editable text. · Intelligent Robots − Robots are able to perform the instructions given by a human.
  9. 9. MOTIVATION: The motivation for every researcher in artificial intelligence (AI) is to match the multi-faceted aspects of intelligent systems and perhaps at some point develop a system that can match human intelligence or even exceed it. The aim of the present work is to advance arguments to substantiate the view that the modeling of cognitive and biological systems should utilize concepts emanating from both the self-organizing and representation list paradigms. Indeed, both paradigms offer only an incomplete account of the nature of life, biological or cognitive, which is given a more realistic explanation when both paradigms are understood as complementary. Philosophical and conceptual arguments are given to substantiate this inclusive position, and how it relates to a web of existing philosophical viewpoints. The position is strengthened by the definition of mathematical and computational formalisms which show its relevance in the creation of practical computational applications for information technology. I intend the present work to be organized in a semiotic way with semantic, syntactic, and pragmatic areas made explicit. This way, the problem is explored philosophically, formally, and computationally respectively. I do not expect the three areas to fully support one another.
  10. 10. The philosophical part lays out the problem in general terms and proposes conceptual arguments that should stand on their own. The formal parts, can also stand alone since they represent mathematical constructs valid in their own right. In any case, they are proposed as formal tools to deal with certain aspects of the larger philosophical issues. Finally, the computational parts give some pragmatic validation to certain aspects of the formal tools, by creating computational models of the larger conceptual issues as well as practical applications valid on their own. These computational applications, useful for the fields of data-mining and optimization algorithms, offer the desired pragmatic validation of the philosophical positions advanced. In so doing, they show that there are important advantages to be gained from more inclusive, complementary, theories of artificial intelligence and artificial life that acknowledge both self-organization and representation. The philosophical and conceptual part of the dissertation starts with a discussion of the divisions between representationalism and self-organization. Self-organization is presented within a framework of emergence and of levels of description.
  11. 11. I introduce the notion of selected self-organization as the backbone of the evolutionary constructivist position, and defend the existence of a symbolic dimension as a pragmatic result to increase the effectiveness of selected self- organization. I further discuss how these ideas relate to the study of natural language and evolutionary systems. Following these ideas, I next propose an evolving semiotic conceptual framework for this inclusive form of self-organization with both representational and constructed facets. With natural language in mind, I develop a mathematical tool based on fuzzy set and evidence theories called evidence set, proposed as a more accurate model of cognitive categorization processes. Evidence sets extend interval valued fuzzy sets to a belief based framework, creating a method of formally modeling the contextual constraints of cognitive categories. Evidence sets are representational artifacts, but are also constrained by subjective belief structures, which are two key elements of evolutionary constructivism. In addition, evidence sets capture all forms of uncertainty recognized in generalized information theory, uncapturable by other set structures. Finally, an extended theory of approximate reasoning is proposed based on set-theoretic operations defined for evidence sets.
  12. 12. With evolutionary systems and artificial life in mind, I discuss the idea of contextual genetic algorithms. These computational models of natural selection are based on the existence of intermediate levels between genotype and phenotype. In other words, genetic descriptions do not encode directly for phenotypic traits, but for the boundary conditions of intermediate dynamical systems which self-organize into a set of phenotypical traits. The indirect encoding of solutions for a particular problem in genetic algorithms is referred to as contextual since the intermediate dynamical systems may depend on inputs other than just the genetic description, such as environmental observables. That is, expression of chromosomes to solutions does not depend solely on genetic information, but also on the system’s context. Indirect genetic encoding is not only a more biologically correct model of genetic natural selection, but it also allows the evolution of different solutions from the same descriptions, which is important for adaptation, and additionally yields tremendous genetic information compression. Furthermore, conceptually, the marriage of selection and self organization is the crux of evolutionary constructivism in evolutionary systems theory.
  13. 13. In order to validate evidence sets and contextual genetic algorithms as relevant models, I explore them computationally in a number of problem areas. Evidence sets are utilized in the development of a search method which acts on several relational databases. This search is based on the reduction of uncertainty stemming from conflicts between the information stored in the various databases which define several contexts. Contextual genetic algorithms are utilized in two distinct models. The first a model of RNA editing which shows that environmental factors can control genetic translation ontogenetically. The second an indirect encoding scheme based on fuzzy logic designed to attain important information compression of genetic descriptions, which is validated in the evolution of neural networks and cellular automata. Both of these models show how the specific materiality of evolutionary systems, or embodiment, both constrains and enable emergent, evolutionary, classification, which is the thrust of evolutionary constructivism.
  15. 15.  Rule-based systems or production systems are computer systems that use rules to provide recommendations or diagnoses, or to determine a course of action in a particular situation or to solve a particular problem.  A rule-based system consists of a number of components: 1. a database of rules (also called a knowledge base) 2. a database of facts 3. an interpreter, or inference engine  In a rule-based system, the knowledge base consists of a set of rules that represent the knowledge that the system has.  The database of facts represents inputs to the system that are used to derive conclusions, or to cause actions.  The interpreter, or inference engine, is the part of the system that controls the process of deriving conclusions. It uses the rules and facts, and combines them together to draw conclusions.
  16. 16.  Using deduction to reach a conclusion from a set of antecedents (background (or) past history) is called forward chaining.  An alternative method, backward chaining, starts from a conclusion and tries to show it by following a logical path backward from the conclusion to a set of antecedents that are in the database of facts.
  17. 17. Forward Chaining: • Forward chaining employs the system starts from a set of facts, and a set of rules, and tries to find a way of using those rules and facts to deduce a conclusion or come up with a suitable course of action. • This is known as data-driven reasoning because the reasoning starts from a set of data and ends up at the goal, which is the conclusion. • When applying forward chaining, the first step is to take the facts in the fact database and see if any combination of these matches all the antecedents of one of the rules in the rule database. • When all the antecedents of a rule are matched by facts in the database, then this rule is triggered. • Usually, when a rule is triggered, it is then fired, which means its conclusion is added to the facts database. • If the conclusion of the rule that has fired is an action or a recommendation, then the system may cause that action to take place or the recommendation to be made. •For example, consider the following set of rules that is used to control an elevator in a three-story building:
  18. 18. Rule 1 IF on first floor AND button is pressed on first floor THEN open door Rule 2 IF on first floor AND button is pressed on second floor THEN go to second floor Rule 3 IF on first floor AND button is pressed on third floor THEN go to third floor Rule 4 IF on second floor AND button is pressed on first floor AND already going to third floor THEN remember to go to first floor later
  19. 19. • This represents just a subset of the rules that would be needed, but we can use it to illustrate how forward chaining works. • Let us imagine that we start with the following facts in our database: Fact 1 At first floor Fact 2 Button pressed on third floor Fact 3 Today is Tuesday
  20. 20. • Now the system examines the rules and finds that Facts 1 and 2 match the antecedents of Rule 3. Hence, Rule 3 fires, and its conclusion “Go to third floor” is added to the database of facts. Presumably, this results in the elevator heading toward the third floor. • Note that Fact 3 was ignored altogether because it did not match the antecedents of any of the rules. • Now let us imagine that the elevator is on its way to the third floor and has reached the second floor, when the button is pressed on the first floor. The fact Button pressed on first floor. • Is now added to the database, which results in Rule 4 firing. • Now let us imagine that later in the day the facts database contains the following information:
  21. 21. Fact 1 At first floor Fact 2 Button pressed on second floor Fact 3 Button pressed on third floor In this case, two rules are triggered—Rules 2 and 3. In such cases where there is more than one possible conclusion, conflict resolution needs to be applied to decide which rule to fire.
  22. 22. Backward Chaining: • Forward chaining applies a set of rules and facts to deduce whatever conclusions can be derived, which is useful when a set of facts are present, but you do not know what conclusions you are trying to prove. • Forward chaining can be inefficient because it may end up proving a number of conclusions that are not currently interesting. • In such cases, where a single specific conclusion is to be proved, backward chaining is more appropriate. • In backward chaining, we start from a conclusion, which is the hypothesis we wish to prove, and we aim to show how that conclusion can be reached from the rules and facts in the database. • The conclusion we are aiming to prove is called a goal, and so reasoning in this way is known as goal-driven reasoning. • Backward chaining is often used in formulating plans.
  23. 23. • A plan is a sequence of actions that a program decides to take to solve a particular problem. • Backward chaining can make the process of formulating a plan more efficient than forward chaining. •Backward chaining in this way starts with the goal state, which is the set of conditions the agent wishes to achieve in carrying out its plan. It now examines this state and sees what actions could lead to it. • For example, if the goal state involves a block being on a table, then one possible action would be to place that block on the table. • This action might not be possible from the start state, and so further actions need to be added before this action in order to reach it from the start state. • In this way, a plan can be formulated starting from the goal and working back toward the start state.
  24. 24. • The benefit in this method is particularly clear in situations where the first state allows a very large number of possible actions. • In this kind of situation, it can be very inefficient to attempt to formulate a plan using forward chaining because it involves examining every possible action, without paying any attention to which action might be the best one to lead to the goal state. • Backward chaining ensures that each action that is taken is one that will definitely lead to the goal, and in many cases this will make the planning process far more efficient.
  26. 26.  An expert system (ES) is a software system that captures human expertise for supporting decision-making; this is useful for dealing with problems involving incomplete information or large amounts of complex knowledge.  Expert systems are particularly useful for on-line operations in the control field because they incorporate symbolic and rule-based knowledge that relate situation and actions, and they also have the ability to explain and justify a line of reasoning.  The ES basically consists of knowledge base, database, reasoning machine, interpretation mechanism, knowledge acquisition and user interface, which is shown in Figure.
  27. 27. Fig: The sketches of the expert system
  28. 28.  An expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise.  An expert system’s knowledge is obtained from expert sources and coded in a form suitable for the system to use in its inference or reasoning processes.  The expert knowledge must be obtained from specialists or other sources of expertise, such as texts, journal, articles and databases.  This type of knowledge usually requires much training and experience in some specialized field such as medicine, geology, system configuration, or engineering design.  Once a sufficient body of expert knowledge has been auquired, it must be encoded in some form, loaded into a knowledge base, then tested, and refined continually throughout the life of the system.
  29. 29. Characteristics Features of Expert Systems: Expert systems differ from conventional computer system in several important ways 1. Expert systems use knowledge rather than data to control the solution process. Much of the knowledge used in heuristic in nature rather than algorithmic. 2. The knowledge is encoded and maintained as an entity separate from the control program. As such, it is not complicated together with the control program itself. This permits the incremental addition and modification of the knowledge base without recompilation of the control programs. Furthermore, it is possible in some cases to use different knowledge bases with the same control programs to produce different types of expert systems. Such systems are known as expert system shells since they may be loaded with different knowledge bases.
  30. 30. 3. Expert systems are capable of explaining how a particular conclusion was reached, and why requested information is needed during a consultation. This is important as it gives the user a chance to assess and understand the systems reasoning ability, thereby improving the user’s confidence in the system. 4. Expert systems use symbolic representations for knowledge and perform their inference through symbolic computations that closely resemble manipulations of natural language. 5. Expert systems often reason with met knowledge, that is, they reason with knowledge about themselves, and their own knowledge limits and capabilities.
  31. 31. Architecture of an Expert System:  Typical expert system architecture is shown in Figure.  The knowledge base contains the specific domain knowledge that is used by an expert to derive conclusions from facts.  In the case of a rule-based expert system, this domain knowledge is expressed in the form of a series of rules.  The explanation system provides information to the user about how the inference engine arrived at its conclusions. This can often be essential, particularly if the advice being given is of a critical nature, such as with a medical diagnosis system.
  32. 32. Fig: Expert System Architecture
  33. 33.  If the system has used faulty reasoning to arrive at its conclusions, then the user may be able to see this by examining the data given by the explanation system.  The fact database contains the case-specific data that are to be used in a particular case to derive a conclusion.  In the case of a medical expert system, this would contain information that had been obtained about the patient’s condition.  The user of the expert system interfaces with it through a user interface, which provides access to the inference engine, the explanation system, and the knowledge-base editor.  The inference engine is the part of the system that uses the rules and facts to derive conclusions. The inference engine will use forward chaining, backward chaining, or a combination of the two to make inferences from the data that are available to it.  The knowledge-base editor allows the user to edit the information that is contained in the knowledge base.  The knowledge-base editor is not usually made available to the end user of the system but is used by the knowledge engineer or the expert to provide and update the knowledge that is contained within the system.