INDEXPAGE NO.

SYLLABUS 2

Unit IV

1.Game playing techniques 3

1.1.Mini max procedure 3

2. Alpha-beta cut-offs 5

3. Planning 6

4. Study of the block world problem in robotics 7

5. Introduction to understanding and natural languages processing 9

Unit V 11

6. Introduction to learning 11

7. Various techniques used in learning 11

8. Introduction to neural networks 17

9. Applications of neural networks 17

9.1. Real-life applications 17

9.2.Neural networks and neuroscience 18

10. Common sense 19

11. Reasoning 20

12. Some example of expert systems 20

12.1. Advantages 21

12.2. Disadvantages 21

REFERENCES 23

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IT 833 Artificial IntelligenceBranch : Information Technology, VIII SemesterCourse: Artificial Intelligence

Unit I: Meaning and definition of artificial intelligence, Various types of production systems,Characteristics of production systems, Study and comparison of breadth first search and depth firstsearch. Techniques, other Search Techniques like hill Climbing, Best first Search. A* algorithm,AO* algorithms etc, and various types of control strategies.

Unit II: Knowledge Representation, Problems in representing knowledge, knowledgerepresentation using propositional and predicate logic, comparison of propositional and predicatelogic, Resolution, refutation, deduction, theorem proving, inferencing, monotonic and nonmonotonicreasoning.

Unit III: Probabilistic reasoning, Baye's theorem, semantic networks, scripts, schemas, frames,conceptual dependency, fuzzy logic, forward and backward reasoning.

Unit IV: Game playing techniques like minimax procedure, alpha-beta cut-offs etc, planning, Studyof the block world problem in robotics, Introduction to understanding and natural languagesprocessing.

Unit V: Introduction to learning, Various techniques used in learning, introduction to neural networks, applications of neural networks, common sense, reasoning, some example of expert systems.

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Unit IV

1. Game playing techniques

Game playing has been a major topic of AI since the very beginning. Beside the attraction of the topic to people, it is also because its close relation to "intelligence", and its well-defined states and rules.

The most common used AI technique in game is search. In some other problem-solving activities, state change is solely caused by the action of the system itself. However, in multi-player games, states also depend on the actions of other players (systems) who usually have different goals.

A special situation that has been studied most is two-person zero-sum game, where the two players have exactly opposite goals, that is, each state can be evaluated by a score from one player's viewpoint, and the other's viewpoint is exactly the opposite. This type of game is common, and easy to analyze, though not all competitions are zero-sum!

There are perfect information games (such as Chess and Go) and imperfect information games (such as Bridge and games where dice are used). Given sufficient time and space, usually an optimum solution can be obtained for the former by exhaustive search, though not for the latter. However, for most interesting games, such a solution is usually too inefficient to be practically used.

2. Mini max procedure

Minimax is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario. Originally formulated for two-player zero-sum game theory, covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision making in the presence of uncertainty.For two-person zero-sum perfect-information game, if the two players take turn to move, the minimax procedure can solve the problem given sufficient computational resources. This algorithm assumes each player takes the best move

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in each step. First, we distinguish two types of nodes, MAX and MIN, in the state graph, determined by the depth of the search tree.

Minimax procedure: starting from the leaves of the tree (with final scores with respect to one player, MAX), and go backwards towards the root (the starting state). At each step, one player (MAX) takes the action that leads to the highest score, while the other player (MIN) takes the action that leads to the lowest score.

All nodes in the tree will all be scored, and the path from root to the actual result is the one on which all nodes have the same score.

Example:

Because of computational resources limitation, the search depth is usually restricted, and estimated scores generated by a heuristic function are used in place of the actual score in the above procedure.

Example: Tic-tac-toe, with the difference of possible win paths as the henristic function.

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3. Alpha-beta cut-offs

Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player games (Tic-tac-toe, Chess, Go, etc.). It stops completely evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move. Such moves need not be evaluated further. When applied to a standard minimax tree, it returns the same move as minimax would, but prunes away branches that cannot possibly influence the final decision.

In Alpha-Beta Pruning, each MAX node has an alpha value, which never decreases; each MIN node has a beta value, which never increases. These values are set and updated when the value of a child is obtained. Search is depth-first, and stops at any MIN node whose beta value is smaller than or equal to the alpha value of its parent, as well as at any MAX node whose alpha value is greater than or equal to the beta value of its parent.

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Examples: in the following partial trees, the other children of node (5) do not need to be generated.

(1)MAX[>=3] ----- (2)MIN[==3] ----- (3)MAX[==5] | |------------ (4)MAX[==3] | |------------ (5)MIN[<=0] ----- (6)MAX[==0] |-----------X |-----------X

(1)MIN[<=5] ----- (2)MAX[==5] ----- (3)MIN[==5] | |------------ (4)MIN[==3] | |------------ (5)MAX[>=8] ----- (6)MIN[==8] |-----------X |-----------XThis method is used in a Prolog program that plays Tic-tac-toe.

4. Planning

The planning problem in Artificial Intelligence is about the decision making performed by intelligent creatures like robots, humans, or computer programs when trying to achieve some goal. It involves choosing a sequence of actions that will (with a high likelihood) transform the state of the world, step by step, so that it will satisfy the goal. The world is typically viewed to consist of atomic facts (state variables), and actions make some facts true and some facts false. In the following we discuss a number of ways of formalizing planning, and show how the planning problem can be solved automatically.

We will only focus on the simplest AI planning problem, characterized by the restriction to one agent in a deterministic environment that can be fully observed. More complex forms of planning can be formalized e.g. in the framework of Marvov decision processes, with uncertainty about the effects of actions and therefore without the possibility to predict the results of a plan with certainty.

The most basic planning problem is one instance of the general s-t reachability problem for succinctly represented transition graphs, which has other important applications in Computer Aided Verification (reachability analysis, model-

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checking), Intelligent Control, discrete event-systems diagnosis, and so on. All of the methods described below are equally applicable to all of these other problems as well, and many of these methods were initially developed and applied in the context of these other problems.

5. Study of the block world problem in robotics

Many areas of Computer Science use simple, abstract domains for both analytical and empirical studies. For example, an early AI study of planning and robotics (STRIPS) used a block world in which a robot arm performed tasks involving the manipulation of blocks. In this problem you will model a simple block world under certain rules and constraints. Rather than determine how to achieve a specified state, you will "program" a robotic arm to respond to a limited set of commands. The problem is to parse a series of commands that instruct a robot arm in how to manipulate blocks that lie on a flat table. Initially there are n blocks on the table (numbered from 0 to n-1) with block bi adjacent to block bi+1 for all 0 <= i < n-1 as shown in the diagram below:

The valid commands for the robot arm that manipulates blocks are:

b move a ontowhere a and b are block numbers, puts block a onto block b after returning any blocks that are stacked on top of blocks a and b to their initial positions.

move a over b where a and b are block numbers, puts block a onto the top of the stack containing block b, after returning any blocks that are stacked on top of block a to their initial positions.

pile a onto b where a and b are block numbers, moves the pile of blocks consisting of block a,

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and any blocks that are stacked above block a, onto block b. All blocks on top of block b are moved to their initial positions prior to the pile taking place. The blocks stacked above block a retain their order when moved. pile a over b where a and b are block numbers, puts the pile of blocks consisting of block a, and any blocks that are stacked above block a, onto the top of the stack containing block b. The blocks stacked above block a retain their original order when moved.

quit terminates manipulations in the block world.Any command in which a = b or in which a and b are in the same stack of blocks is an illegal command. All illegal commands should be ignored and should have no affect on the configuration of blocks.

Input

The input begins with an integer n on a line by itself representing the number of blocks in the block world. You may assume that 0 < n < 25. The number of blocks is followed by a sequence of block commands, one command per line. Your program should process all commands until the quit command is encountered.

You may assume that all commands will be of the form specified above. There will be no syntactically incorrect commands.

Output

The output should consist of the final state of the blocks world. Each original block position numbered i ( 0 <= i < n where n is the number of blocks) should appear followed immediately by a colon. If there is at least a block on it, the colon must be followed by one space, followed by a list of blocks that appear stacked in that position with each block number separated from other block numbers by a space. Don't put any trailing spaces on a line.

There should be one line of output for each block position (i.e., n lines of output where n is the integer on the first line of input).

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Sample Input

10move 9 onto 1move 8 over 1move 7 over 1move 6 over 1pile 8 over 6pile 8 over 5move 2 over 1move 4 over 9quit

Sample Output

0: 01: 1 9 2 42:3: 34:5: 5 8 7 66:7:8:9:

6. Introduction to understanding and natural languages processing

Natural language understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension.

The process of disassembling and parsing input is more complex than the reverse process of assembling output in natural language generation because of the occurrence of unknown and unexpected features in the input and the need to determine the appropriate syntactic and semantic schemes to apply to it, factors which are pre-determined when outputting language.[dubious – discuss]

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There is considerable commercial interest in the field because of its application to news-gathering, text categorization, voice-activation, archiving and large-scale content-analysis.

The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to robots, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require in depth understanding of the text, but is far more complex than the management of simple queries to database tables with fixed schemata

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Unit V

1. Introduction to learning

Learning can be described as normally a relatively permanent change that occursin behavior as a result of experience. Learning occurs in various regimes. Forexample, it is possible to learn to open a lock as a result of trial and error;possible to learn how to use a word processor as a result of following particularinstructions.Once the internal model of what ought to happen is set, it is possible to learn bypracticing the skill until the performance converges on the desired model. Onebegins by paying attention to what needs to be done, but with more practice, onewill need to monitor only the trickier parts of the performance.Automatic performance of some skills by the brain points out that the brain iscapable of doing things in parallel i.e. one part is devoted to the skill whilstanother part mediates conscious experience.There's no decisive definition of learning but here are some that do justice:·  "Learning denotes changes in a system that ... enables a system to do thesame task more efficiently the next time." --Herbert Simon·  "Learning is constructing or modifying representations of what is beingexperienced." --Ryszard Michalski·  "Learning is making useful changes in our minds." --Marvin Minsky

2. Various techniques used in learning

The following components are part of any learning problem:

Task :-The behavior or task that is being improved Data :- The experiences that are used to improve performance in the task measure of improvement, How the improvement is measured - for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed , Consider the agent internals of Figure .1.

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Figure 1.: Offline and online decomposition of an agent

The problem of learning is to take in prior knowledge and data (e.g., about the experiences of the agent) and to create an internal representation (the knowledge base) that is used by the agent as it acts. This internal representation could be the raw experiences themselves, but it is typically a compact representation that summarizes the data. The problem of inferring an internal representation based on examples is often called induction and can be contrasted with deduction, which is deriving consequences of a knowledge base, and abduction(Abduction is a form of reasoning where assumptions are made to explain observations. For example, if an agent were to observe that some light was not working, it can hypothesize what is happening in the world to explain why the light was not working. An intelligent tutoring system could try to explain why a student gives some answer in terms of what the student understands and does not understand.), which is hypothesizing what may be true about a particular case.

There are two principles that are at odds in choosing a representation scheme:

The richer the representation scheme, the more useful it is for subsequent problems solving. For an agent to learn a way to solve a problem, the representation must be rich enough to express a way to solve the problem.

The richer the representation, the more difficult it is to learn. A very rich representation is difficult to learn because it requires a great deal of data, and often many different hypotheses are consistent with the data.

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The representations required for intelligence are a compromise between many desiderata (see Section 2).

Figure 2: The role of representations in solving problems

The ability to learn the representation is one of them, but it is not the only one.

Learning techniques face the following issues:

TaskVirtually any task for which an agent can get data or experiences can be learned. The most commonly studied learning task is supervised learning:given some input features, some target features, and a set of training examples where the input features and the target features are specified, predict the target features of a new example for which the input features are given. This is called classification when the target variables are discrete and regression when the target features are continuous.

Other learning tasks include learning classifications when the examples are not already classified (unsupervised learning), learning what to do based on rewards and punishments (reinforcement learning), learning to reason faster (analytic learning), and learning richer representations such as logic programs (inductive logic programming) or Bayesian networks.

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FeedbackLearning tasks can be characterized by the feedback given to the learner. In supervised learning, what has to be learned is specified for each example. Supervised classification occurs when a trainer provides the classification for each example. Supervised learning of actions occurs when the agent is given immediate feedback about the value of each action. Unsupervised learning occurs when no classifications are given and the learner must discover categories and regularities in the data. Feedback often falls between these extremes, such as in reinforcement learning, where the feedback in terms of rewards and punishments occurs after a sequence of actions. This leads to the credit-assignment problem of determining which actions were responsible for the rewards or punishments. For example, a user could give rewards to the delivery robot without telling it exactly what it is being rewarded for. The robot then must either learn what it is being rewarded for or learn which actions are preferred in which situations. It is possible that it can learn what actions to perform without actually determining which consequences of the actions are responsible for rewards.

Representation

For an agent to use its experiences, the experiences must affect the agent's internal representation. Much of machine learning is studied in the context of particular representations (e.g., decision trees, neural networks, or case bases). This chapter presents some standard representations to show the common features behind learning.

Online and offline

In offline learning, all of the training examples are available to an agent before it needs to act. In online learning, training examples arrive as the agent is acting. An agent that learns online requires some representation of its previously seen examples before it has seen all of its examples. As new examples are observed, the agent must update its representation. Typically, an agent never sees all of its examples. Active learning is a form of online learning in which the agent acts to acquire useful examples from which to learn. In active learning, the agent reasons about which examples would be useful to learn from and acts to collect these examples.

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Measuring success

Learning is defined in terms of improving performance based on some measure. To know whether an agent has learned, we must define a measure of success. The measure is usually not how well the agent performs on the training experiences, but how well the agent performs for new experiences.

In classification, being able to correctly classify all training examples is not the problem. For example, consider the problem of predicting a Boolean feature based on a set of examples. Suppose that there were two agents P and N. Agent P claims that all of the negative examples seen were the only negative examples and that every other instance is positive. Agent N claims that the positive examples in the training set were the only positive examples and that every other instance is negative. Both of these agents correctly classify every example in the training set but disagree on every other example. Success in learning should not be judged on correctly classifying the training set but on being able to correctly classify unseen examples. Thus, the learner must generalize: go beyond the specific given examples to classify unseen examples.

A standard way to measure success is to divide the examples into a training set and a test set. A representation is built using the training set, and then the predictive accuracy is measured on the test set. Of course, this is only an approximation of what is wanted; the real measure is its performance on some future task.

BiasThe tendency to prefer one hypothesis over another is called a bias.Consider the agents N and P defined earlier. Saying that a hypothesis is better than N's or P's hypothesis is not something that is obtained from the data - both N and P accurately predict all of the data given - but is something external to the data. Without a bias, an agent will not be able to make any predictions on unseen examples. The hypotheses adopted by P and N disagree on all further examples, and, if a learning agent cannot choose some hypotheses as better, the agent will not be able to resolve this disagreement. To have any inductive process make predictions on unseen data, an agent requires a bias. What constitutes a good bias is an empirical question about which biases work best in practice; we do not imagine that either P's or N's biases work well in practice.

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Learning as search

Given a representation and a bias, the problem of learning can be reduced to one of search. Learning is a search through the space of possible representations, trying to find the representation or representations that best fits the data given the bias. Unfortunately, the search spaces are typically prohibitively large for systematic search, except for the simplest of examples. Nearly all of the search techniques used in machine learning can be seen as forms of local search through a space of representations. The definition of the learning algorithm then becomes one of defining the search space, the evaluation function, and the search method.

Noise

In most real-world situations, the data are not perfect. Noise exists in the data (some of the features have been assigned the wrong value), there are inadequate features (the features given do not predict the classification), and often there are examples with missing features. One of the important properties of a learning algorithm is its ability to handle noisy data in all of its forms.

Interpolation and extrapolation

For cases in which there is a natural interpretation of "between," such as where the prediction is about time or space, interpolation involves making a prediction between cases for which there are data. Extrapolation involves making a prediction that goes beyond the seen examples. Extrapolation is usually much more inaccurate than interpolation. For example, in ancient astronomy, the Ptolemaic system and heliocentric system of Copernicus made detailed models of the movement of solar system in terms of epicycles (cycles within cycles). The parameters for the models could be made to fit the data very well and they were very good at interpolation; however, the models were very poor at extrapolation. As another example, it is often easy to predict a stock price on a certain day given data about the prices on the days before and the days after that day. It is very difficult to predict the price that a stock will be tomorrow, and it would be very profitable to be able to do so. An agent must be careful if its test cases mostly involve interpolating between data points, but the learned model is used for extrapolation.

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3. Introduction to neural networks

Artificial Intelligence has had its fair share from the field of neuroscience. Neuroscience is the study of nervous system, particularly the brain. How the brain enables human beings to think has remained a mystery until the present day. But significant leaps and bounds in the field have enabled scientists to come close to the nature of thought processes inside a brain.

Neural Network research has gone through a number of these lulls, as new methods have been created have shown brief promise, have been over-promoted, and have suffered from some setback. However scientists have always come back to the technology because it is a real attempt to model neural mechanisms despite the hype.

Neural Networks can be loosely separated into Neural Models, Network Models and Learning Rules. the earliest mathematical models of the Neuron pre-date Mcullock and Pitts who developed the first Network models to explain how the signals passed from one neuron to another within the network. When you hear of a network being described as a feed forward or feedback network, they are describing how the network connects neurons in one layer to neurons in the next. Weiners work allowed Mculloch and Pitts to describe how these different connection types would affect the operation of the network.

4. Applications of neural networks

The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations. This is particularly useful in applications where the complexity of the data or task makes the design of such a function by hand impractical.

a). Real-life applications

The tasks artificial neural networks are applied to tend to fall within the following broad categories:

Function approximation, or regression analysis, including time series prediction, fitness approximation and modeling.

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Classification, including pattern and sequence recognition, novelty detection and sequential decision making.

Data processing, including filtering, clustering, blind source separation and compression.

Robotics, including directing manipulators, prosthesis. Control, including Computer numerical control.

Application areas include the system identification and control (vehicle control, process control, natural resources management), quantum chemistry,[41] game-playing and decision making (backgammon, chess, poker), pattern recognition (radar systems, face identification, object recognition and more), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications (e.g. automated trading systems), data mining (or knowledge discovery in databases, "KDD"), visualization and e-mail spam filtering.

Artificial neural networks have also been used to diagnose several cancers. An ANN based hybrid lung cancer detection system named HLND improves the accuracy of diagnosis and the speed of lung cancer radiology.[42] These networks have also been used to diagnose prostate cancer. The diagnoses can be used to make specific models taken from a large group of patients compared to information of one given patient. The models do not depend on assumptions about correlations of different variables. Colorectal cancer has also been predicted using the neural networks. Neural networks could predict the outcome for a patient with colorectal cancer with more accuracy than the current clinical methods. After training, the networks could predict multiple patient outcomes from unrelated institutions.[43]

b). Neural networks and neuroscience

Theoretical and computational neuroscience is the field concerned with the theoretical analysis and the computational modeling of biological neural systems. Since neural systems are intimately related to cognitive processes and behavior, the field is closely related to cognitive and behavioral modeling.

The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (biological neural network models) and theory (statistical learning theory and information theory).

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5. Common sense

In artificial intelligence research, commonsense knowledge is the collection of facts and information that an ordinary person is expected to know. The commonsense knowledge problem is the ongoing project in the field of knowledge representation (a sub-field of artificial intelligence) to create a commonsense knowledge base: a database containing all the general knowledge that most people possess, represented in a way that it is available to artificial intelligence programs that use natural language or make inferences about the ordinary world. Such a database is a type of ontology of which the most general are called upper ontologies.

The problem is considered to be among the hardest in all of AI research because the breadth and detail of commonsense knowledge is enormous. Any task that requires commonsense knowledge is considered AI-complete: to be done as well as a human being does it, it requires the machine to appear as intelligent as a human being. These tasks include machine translation, object recognition, text mining and many others. To do these tasks perfectly, the machine simply has to know what the text is talking about or what objects it may be looking at, and this is impossible in general unless the machine is familiar with all the same concepts that an ordinary person is familiar with.

Information in a commonsense knowledge base may include, but is not limited to, the following:

An ontology of classes and individuals Parts and materials of objects Properties of objects (such as color and size) Functions and uses of objects Locations of objects and layouts of locations Locations of actions and events Durations of actions and events Preconditions of actions and events Effects (postconditions) of actions and events Subjects and objects of actions Behaviors of devices Stereotypical situations or scripts Human goals and needs

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Emotions Plans and strategies Story themes Contexts

6. Reasoning

To reason is to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive. An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore he is in the museum,” and of the latter, “Previous accidents of this sort were caused by instrument failure; therefore this accident was caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case the truth of the premise lends support to the conclusion without giving absolute assurance. Inductive reasoning is common in science, where data are collected and tentative models are developed to describe and predict future behaviour—until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic, where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.

There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, true reasoning involves more than just drawing inferences; it involves drawing inferences relevant to the solution of the particular task or situation.

7. Some example of expert systems

In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software. An expert system is divided into two sub-systems: the inference engine and the knowledge base. The

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knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging capabilities.

a). Advantages

The goal of knowledge-based systems is to make the critical information required for the system to work explicit rather than implicit. In a traditional computer program the logic is embedded in code that can typically only be reviewed by an IT specialist. With an expert system the goal was to specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts. The benefits of this explicit knowledge representation were rapid development and ease of maintenance.

Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by removing the need to write conventional code many of the normal problems that can be caused by even small changes to a system could be avoided with expert systems. Essentially, the logical flow of the program (at least at the highest level) was simply a given for the system, simply invoke the inference engine. This also was a reason for the second benefit: rapid prototyping. With an expert system shell it was possible to enter a few rules and have a prototype developed in days rather than the months or year typically associated with complex IT projects.

b). Disadvantages

The most common disadvantage cited for expert systems in the academic literature is the knowledge acquisition problem. Obtaining the time of domain experts for any software application is always difficult but for expert systems it was especially difficult because the experts were by definition highly valued and in constant demand by the organization. As a result of this problem a great deal of research in the later years of expert systems was focused on tools for knowledge acquisition, to help automate the process of designing, debugging, and maintaining rules defined by experts. However, when looking at the life-cycle of expert systems in actual use other problems seem at least as critical as knowledge acquisition. These problems were essentially the same as those of any other large system: integration, access to large databases, and performance.

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Performance was especially problematic because early expert systems were built using tools such as Lisp, which executed interpreted rather than compiled code. Interpreting provided an extremely powerful development environment but with the drawback that it was virtually impossible to match the efficiency of the fastest compiled languages of the time, such as C. System and database integration were difficult for early expert systems because the tools were mostly in languages and platforms that were neither familiar to nor welcomed in most corporate IT environments – programming languages such as Lisp and Prolog and hardware platforms such as Lisp Machines and personal computers. As a result a great deal of effort in the later stages of expert system tool development was focused on integration with legacy environments such as COBOL, integration with large database systems, and porting to more standard platforms. These issues were resolved primarily by the client-server paradigm shift as PCs were gradually accepted in the IT world as a legitimate platform for serious business system development and as affordable minicomputer servers provided the processing power needed for AI applications.

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References:-

1. Rich E and Knight K, “Artificial Intelligence”, TMH, New Delhi.2. Nelsson N.J., “Principles of Artificial Intelligence”, Springer Verlag,

Berlin.

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