AI unit iv

àNon-linear planning

This planning is used to set a goal stack and is included in the search space of all possible subgoal orderings. It handles the goal interactions by interleaving method. 

Advantage of non-Linear planning

Non-linear planning may be an optimal solution with respect to plan length (depending on search strategy used). 

Disadvantages of Nonlinear planning

·         It takes larger search space, since all possible goal orderings are taken into consideration.

·         Complex algorithm to understand.

Algorithm 
1. Choose a goal 'g' from the goalset 
2. If 'g' does not match the state, then

         Choose an operator 'o' whose add-list matches goal g

Push 'o' on the opstack

Add the preconditions of 'o' to the goalset

3. While all preconditions of operator on top of opstack are met in state

Pop operator o from top of opstack

state = apply(o, state)

plan = [plan; o]

NonLinear Planning using Constraint posting

• Problems such as this one require subproblems to be worked on simultaneously.
• Thus a nonlinear plan using heuristics such as:

1.     Try to achieve ON(A,B) clearing block A putting block C on the table.

2.     Achieve ON(B,C) by stacking block B on block C.

3.     Complete ON(A,B) by stacking block A on block B.

Constraint posting has emerged as a central technique in recent planning systems

Constraint posting builds up a plan by:

• suggesting operators,
• trying to order them, and
• produce bindings between variables in the operators and actual blocks.

The initial plan consists of no steps and by studying the goal state ideas for the possible steps are generated.

There is no order or detail at this stage.

In this problem means-end analysis suggests two steps with end conditions ON(A,B) and ON(B,C) which indicates the operator STACK giving the layout shown below where the operator is preceded by its preconditions and followed by its post conditions:

                            CLEAR(B)                                       CLEAR(C)

                        *HOLDING(A)                                              *HOLDING(B)

                         STACK(A,B)                                      STACK(B,C)

                         ARMEMPTY                                      ARMEMPTY

                         ON(A,B)                                             ON(B,C)

                        CLEAR(B)                                           CLEAR(C)

                         HOLDING(A)                                     HOLDING(B)

àHierarchical Planning

Principle

§  hierarchical organization of 'actions'

§  complex and less complex (or: abstract) actions

§  lowest level reflects directly executable actions

Procedure

§  planning starts with complex action on top

§  plan constructed through action decomposition

§  substitute complex action with plan of less complex actions (pre-defined plan schemata; or learning of plans/plan abstraction)

• overall plan must generate effect of complex action

The algorithm: Top-down hierarchical planning

n  Search our library of plan operators for ways of achieving the goal

n  For an operator to be usable, the preconditions much match the ‘state of the world’

n  For an operator to be useful, the effect must leave us nearer to achieving our goal than we were before!

 Example1:

              

   

Example2 : To bulid a house,hierarchic al decomposition can be shown

Detailed Decomposition of Plan

Hierarchy of actions

n  In terms of major action or minor action

n     Lower level activities would detail more precise steps         for accomplishing the higher level tasks.

Ex:Planning for ”Going to Goa this Cristmas”

      

      Major Steps :

n  Hotel Booking

n  Ticket Booking

n  Reaching Goa

n  Staying and enjoying there

n  Coming Back

        Minor Steps :

n  Take a taxi to reach station / airport

n  Have candle light dinner on beach

n  Take photos

Actions required for “Travelling to Goa”:

n  Opening makemytrip.com (1)

n  Finding flight (2)

n  Buy Ticket (3)

n  Get taxi(2)

n  Reach airport(3)

n  Pay-driver(1)

n  Check in(1)

n  Boarding plane(2)

n  Reach Goa(3)

Chapter -15

Natural Language Processing (NLP)

Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.

Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.

Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to understand and process human languages. The field of NLP involves making computers to perform useful tasks with the natural languages humans use. The input and output of an NLP system can be –

• Speech
• Written Text

A typical interaction between humans and machines using Natural Language Processing could go as follows:

1. A human talks to the machine

2. The machine captures the audio

3. Audio to text conversion takes place

4. Processing of the text’s data

5. Data to audio conversion takes place

6. The machine responds to the human by playing the audio file

Natural Language Processing is the driving force behind the following common applications:

• Language translation applications such as Google Translate
• Word Processors such as Microsoft Word and Grammarly that employ NLP to check grammatical accuracy of texts.
• Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests.

àComponents of NLP

There are two components of NLP as given –

Natural Language Understanding (NLU)

Understanding involves the following tasks −

• Mapping the given input in natural language into useful representations.
• Analyzing different aspects of the language.

Natural Language Generation (NLG)

It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.

It involves –

• Text planning − It includes retrieving the relevant content from knowledge base.
• Sentence planning − It includes choosing required words, forming meaningful phrases, setting tone of the sentence.
• Text Realization − It is mapping sentence plan into sentence structure.

The NLU is harder than NLG.

Components of NLG

Difficulties in NLU

NL has an extremely rich form and structure.

It is very ambiguous. There can be different levels of ambiguity −  

(i)Lexical ambiguity − It is at very primitive level such as word-level.

For example, treating the word “board” as noun or verb?

(ii)Syntax Level ambiguity − A sentence can be parsed in different ways.

    

  For example, “He lifted the beetle with red cap.” − Did he use cap to lift the       

                          beetle or    “he lifted a beetle that had red cap”?

(iii) Referential ambiguity − Referring to something using pronouns.

     For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired?

 (iv) One input can mean different meanings.

(iv) Many inputs can mean the same thing.

                                                Phases of NLP architecture  

Applications of NLP

Natural Language Processing can be applied into various areas like Machine Translation, Email Spam detection, Information Extraction, Summarization, Question Answering etc.

i.                   Machine Translation

ii.                 Text Categorization

iii.              Spam Filtering

iv.              Information Extraction

àSteps in NLP

There are general five steps −

Lexical Analysis − It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of txt into paragraphs, sentences, and words.

ORDER

Order number  Date ordered  Date shipped

4290                 2/2/02             2/2/02

4291                 2/2/02             2/2/02

4292                 2/2/02

USER: Has my order number 4291 been shipped yet?

DB QUERY: order(number=4291,date shipped=?)

RESPONSE TO USER: Order number 4291 was shipped on 2/2/02

It might look quite easy to write patterns for these queries, but very similar strings can mean very different things,

while very different strings can mean much the same thing. 1 and 2 below look very similar but mean something completely different, while 2 and 3 look very different but mean much the same thing.

·         Syntactic Analysis (Parsing) − It involves analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words.

The sentence such as “The school goes to boy” is rejected by English syntactic analyzer.

·         Semantic Analysis − It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain.

The semantic analyzer disregards sentence such as “hot ice-cream”.

·         Discourse Integration − The meaning of any sentence depends upon the meaning of the sentence just before it. In addition, it also brings about the meaning of immediately succeeding sentence.

·         Pragmatic Analysis − During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real world knowledge.

à Syntactic Analysis or Syntactic processing

Def: Syntactic analysis is defined as analysis that tells us the logical meaning of certain given sentences or parts of those sentences. We also need to consider rules of grammar in order to define the logical meaning as well as correctness of the sentences

There are a number of algorithms researchers have developed for syntactic analysis, but we consider only the following simple methods –

• Context-Free Grammar
• Top-Down Parser

Let us see them in detail −

Context-Free Grammar

It is the grammar that consists rules with a single symbol on the left-hand side of the rewrite rules.

 Let us create grammar to parse a sentence −

“The bird pecks the grains”

Articles (DET) − a | an | the

Nouns − bird | birds | grain | grains

Noun Phrase (NP) − Article + Noun | Article + Adjective + Noun

= DET N | DET ADJ N

Verbs − pecks | pecking | pecked

Verb Phrase (VP) − NP V | V NP

Adjectives (ADJ) − beautiful | small | chirping

The parse tree breaks down the sentence into structured parts so that the computer can easily understand and process it. In order for the parsing algorithm to construct this parse tree, a set of rewrite rules, which describe what tree structures are legal, need to be constructed.

These rules say that a certain symbol may be expanded in the tree by a sequence of other symbols. According to first order logic rule, if there are two strings Noun Phrase (NP) and Verb Phrase (VP), then the string combined by NP followed by VP is a sentence. The rewrite rules for the sentence are as follows −

S → NP VP

NP → DET N | DET ADJ N

VP → V NP

Lexocon −

DET → a | the

ADJ → beautiful | perching

N → bird | birds | grain | grains

V → peck | pecks | pecking

The parse tree can be created as shown −

Now consider the above rewrite rules. Since V can be replaced by both, "peck" or "pecks", sentences such as "The bird peck the grains" can be wrongly permitted. i. e. the subject-verb agreement error is approved as correct.

Merit − The simplest style of grammar, therefore widely used one.

Demerits −

• They are not highly precise. For example, “The grains peck the bird”, is a syntactically correct according to parser, but even if it makes no sense, parser takes it as a correct sentence.
• To bring out high precision, multiple sets of grammar need to be prepared. It may require a completely different sets of rules for parsing singular and plural variations, passive sentences, etc., which can lead to creation of huge set of rules that are unmanageable.

Top-Down Parser

Here, the parser starts with the S symbol and attempts to rewrite it into a sequence of terminal symbols that matches the classes of the words in the input sentence until it consists entirely of terminal symbols.

These are then checked with the input sentence to see if it matched. If not, the process is started over again with a different set of rules. This is repeated until a specific rule is found which describes the structure of the sentence.

Merit − It is simple to implement.

Demerits −

• It is inefficient, as the search process has to be repeated if an error occurs.
• Slow speed of working.

àSemantic Analysis

Semantic analysis describes the process of understanding natural language–the way that humans communicate–based on meaning and context. 

(i)The semantic analysis of natural language content starts by reading all of the words in content to capture the real meaning of any text.

(ii)It identifies the text elements and assigns them to their logical and grammatical role.

(iii) It analyzes context in the surrounding text and it analyzes the text structure to accurately disambiguate the proper meaning of words that have more than one definition.

Because semantic analysis and natural language processing can help machines automatically understand text,

Semantics is the study of the meaning of words, and semantic analysis is the analysis we use to extract meaning

Semantic analysis involves building up a representation of the objects and actions that a sentence is describing, including details provided by adjectives, adverbs, and prepositions.

Hence, after analyzing the sentence “The black cat sat on the mat”, the system would use a semantic net such as  shown in Figure  to represent the objects and the relationships between them.

A more sophisticated semantic network is likely to be formed, which includes information about the nature of a cat (a cat is an object, an animal, a quadruped, etc.) that can be used to deduce facts about the cat (e.g., that it likes to drink milk).

(i)Lexical ambiguity

Lexical ambiguity occurs when a word has more than one possible meaning. For example, a bat can be a flying mammal or a piece of sporting equipment.

(ii)Semantic ambiguity

Semantic ambiguity occurs when a sentence has more than one possible meaning—often as a result of a syntactic ambiguity.

for example, the sentence “Jane carried the girl with the spade”, the sentence has two different parses, which correspond to two possible meanings for the sentence.

(iii)Referential ambiguity

Referential ambiguity occurs when we use anaphoric expressions, or pronouns to refer to objects that have already been discussed. An anaphora occurs when a word or phrase is used to refer to something without naming it. The problem of ambiguity occurs where it is not immediately clear which object is being referred to. For example, consider the following sentences:

Ex:John gave Bob the sandwich. He smiled.

It is not at all clear from this who smiled—it could have been John or Bob. In general, English speakers or writers avoid constructions such as this to avoid humans becoming confused by the ambiguity. In spite of this, ambiguity can also occur in a similar way where a human would not have a problem, such as

Ex:John gave the dog the sandwich. It wagged its tail.

 In this case, a human listener would know very well that it was the dog that wagged its tail, and not the sandwich. Without specific world knowledge, the natural language processing system might not find it so obvious.

The process by which a natural language processing system determines which meaning is intended by an ambiguous utterance is known as disambiguation.

Disambiguation can be done in a number of ways. One of the most effective ways to overcome many forms of ambiguity is to use probability.

This can be done using prior probabilities or conditional probabilities.

(i) Prior probability might be used to tell the system that the word bat nearly always means a piece of sporting equipment.

     (ii)Conditional probability would tell it that when the word bat is used by a  

          sports fan, this is likely to be the case, but that when it is spoken by a  

          naturalist it is more likely to be a winged mammal.

Disambiguation thus requires a good world model, which contains knowledge about the world that can be used to determine the most likely meaning of a given word or sentence.

The world model would help the system to understand that the sentence “Jane carried the girl with the spade” is unlikely to mean that Jane used the spade to carry the girl because spades are usually used to carry smaller things than girls.

Chapter -20-Expert Systems

What are Expert Systems ? Explain the architecture of  Expert Systems

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Characteristics of Expert Systems

• High performance
• Understandable
• Reliable
• Highly responsive

Capabilities of Expert Systems

The expert systems are capable of −

• Advising
• Instructing and assisting human in decision making
• Demonstrating
• Deriving a solution
• Diagnosing
• Explaining
• Interpreting input
• Predicting results
• Justifying the conclusion
• Suggesting alternative options to a problem

They are incapable of −

• Substituting human decision makers
• Possessing human capabilities
• Producing accurate output for inadequate knowledge base
• Refining their own knowledge

Components of Expert Systems

The components of ES include −

• Knowledge Base
• Inference Engine
• User Interface

(i)Knowledge Base

It contains domain-specific and high-quality knowledge.

Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge.

What is Knowledge?

The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.

Components of Knowledge Base

The knowledge base of an ES is a store of both, factual and heuristic knowledge.

·        Factual Knowledge − It is the information widely accepted by the Knowledge Engineers and scholars in the task domain.

·         Heuristic Knowledge − It is about practice, accurate judgement, one’s ability of evaluation, and guessing.

Knowledge representation

It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.

 Knowledge Acquisition

The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base.

The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers

(ii)Inference Engine

Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.

In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.

In case of rule based ES, it −

·        Applies rules repeatedly to the facts, which are obtained from earlier rule application.

·        Adds new knowledge into the knowledge base if required.

·        Resolves rules conflict when multiple rules are applicable to a particular case.

To recommend a solution, the Inference Engine uses the following strategies −

• Forward Chaining
• Backward Chaining

Forward Chaining

It is a strategy of an expert system to answer the question, “What can happen next?”

Here, the Inference Engine follows the chain of conditions and derivations and finally deduces the outcome. It considers all the facts and rules, and sorts them before concluding to a solution.

This strategy is followed for working on conclusion, result, or effect. For example, prediction of share market status as an effect of changes in interest rates.

Backward Chaining

With this strategy, an expert system finds out the answer to the question, “Why this happened?”

On the basis of what has already happened, the Inference Engine tries to find out which conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason. For example, diagnosis of blood cancer in humans.

(iii)User Interface

User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.

It explains how the ES has arrived at a particular recommendation. The explanation may appear in the following forms −

• Natural language displayed on screen.
• Verbal narrations in natural language.
• Listing of rule numbers displayed on the screen.

The user interface makes it easy to trace the credibility of the deductions.

Benefits of Expert Systems

·        Availability − They are easily available due to mass production of software.

·        Less Production Cost − Production cost is reasonable. This makes them affordable.

·        Speed − They offer great speed. They reduce the amount of work an individual puts in.

·        Less Error Rate − Error rate is low as compared to human errors.

·        Reducing Risk − They can work in the environment dangerous to humans.

·        Steady response − They work steadily without getting motional, tensed or fatigued.

àExpert System Shells

An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components.

 Shell components and description

 The generic components of a shell :

The knowledge acquisition, the knowledge Base, the reasoning, the explanation and the user interface are shown below. The knowledge base and reasoning engine are the core components.

Knowledge Base

A store of factual and heuristic knowledge. Expert system tool provides one or more knowledge representation schemes for expressing knowledge about the application domain. Some tools use both Frames (objects) and IF-THEN rules. In PROLOG the knowledge is represented as logical statements.

Reasoning Engine

Inference mechanisms for manipulating the symbolic information and knowledge in the knowledge base form a line of reasoning in solving a problem. The inference mechanism can range from simple modus ponens backward chaining of IF-THEN rules to Case-Based reasoning.

Knowledge Acquisition subsystem

A subsystem to help experts in build knowledge bases. However, collecting knowledge, needed to solve problems and build the knowledge base, is the biggest bottleneck in building expert systems.

Explanation subsystem

A subsystem that explains the system's actions. The explanation can range from how the final or intermediate solutions were arrived at justifying the need for additional data.

User Interface

A means of communication with the user. The user interface is generally not a part of the expert system technology. It was not given much attention in the past. However, the user interface can make a critical difference in the pe eived utility of an Expert system.

 Important questions

à(Chapter1)What is Artificial Intelligence?

According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

Goals of AI

·        To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

·        To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.

Applications of AI

AI has been dominant in various fields such as −

·        Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

An example is Deep Blue, an IBM chess program that can identify pieces on the chess board and can make predictions accordingly. But the major fault with this is that it has no memory and cannot use past experiences to inform future ones. 

·        Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.

·        Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Ex:One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed.

·        Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example,

o   A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

o   Doctors use clinical expert system to diagnose the patient.

o   Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

·        Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

·        Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

·        Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

à(2^nd chapter)PROBLEM CHARACTERISTICS

Heuristic search is a very general method applicable to a large class of problem . It includes a variety of techniques. In order to choose an appropriate method, it is necessary to analyze the problem with respect to the following considerations.

Is the problem decomposable ?

A very large and composite problem can be easily solved if it can be broken into smaller problems and recursion could be used. Suppose we want to solve.

Ex:- ∫ x2 + 3x+sin2x cos 2x dx

This can be done by breaking it into three smaller problems and solving each by applying specific rules. Adding the results the complete solution is obtained.

2. Can solution steps be ignored or undone?

Problem fall under three classes

(i) ignorable

(ii) recoverable and

(iii)irrecoverable.

(i)ignorable

This classification is with reference to the steps of the solution to a problem.

Consider theorem proving. We may later find that it is of no help. We can still proceed further, since nothing is lost by this redundant step. This is an example of “ignorable” solutions steps.

(ii) recoverable

Now consider the 8 puzzle problem tray and arranged in specified order. While moving from the start state towards goal state, we may undo the unwanted move. This only involves additional steps and the solution steps are recoverable.

(iii) irrecoverable

Lastly consider the game of chess. If a wrong move is made, it can neither be ignored nor be recovered. The thing to do is to make the best use of current situation and proceed. This is an example of an irrecoverable solution steps.

1. Ignorable problems Ex:- theorem proving

                                         · In which solution steps can be ignored.

2. Recoverable problems Ex:- 8 puzzle

                                         · In which solution steps can be undone

3. Irrecoverable problems Ex:- Chess

                                         · In which solution steps can’t be undone

A knowledge of these will help in determining the control structure.

3.. Is the Universal Predictable?

Problems can be classified into those with certain outcome (8- puzzle and water jug problems) and those with uncertain outcome ( playing cards) .

Thus one of the hardest types of problems to solve is the irrecoverable, uncertain – outcome problems ( Ex:- Playing cards).

4. Is good solution absolute or relative ?

                                            (Is the solution a state or a path ?)

There are two categories of problems. In one, like the water jug and 8 puzzle problems, we are satisfied with the solution, unmindful of the solution path taken,

whereas in the other category not just any solution is acceptable. We want the best, like that of traveling sales man problem, where it is the shortest path.

5. The knowledge base consistent ?

In some problems the knowledge base is consistent and in some it is not. For example consider the case when a Boolean expression is evaluated.

 The knowledge base now contains theorems and laws of Boolean Algebra which are always true. On the contrary consider a knowledge base that contains facts about production and cost. These keep varying with time.

Hence many reasoning schemes that work well in consistent domains are not appropriate in inconsistent domains.

Ex. Boolean expression evaluation.

6. What is the role of Knowledge?

Though one could have unlimited computing power, the size of the knowledge base available for solving the problem does matter in arriving at a good solution.

Take for example the game of playing chess, just the rules for determining legal moves and some simple control mechanism is sufficient to arrive at a solution.

But additional knowledge about good strategy and tactics could help to constrain the search and speed up the execution of the program. The solution would then be realistic.

Consider the case of predicting the political trend. This would require an enormous amount of knowledge even to be able to recognize a solution , leave alone the best.

Ex:- 1. Playing chess 2. News paper understanding

7. Does the task requires interaction with the person.

The problems can again be categorized under two heads.

1. Solitary in which the computer will be given a problem description and will produce an answer, with no intermediate communication and with he demand for an explanation of the reasoning process.

Simple theorem proving falls under this category . given the basic rules and laws, the theorem could be proved, if one exists.

Ex:- theorem proving (give basic rules & laws to computer)

2. Conversational, in which there will be intermediate communication between a person and the computer, wither to provide additional assistance to the computer or to provide additional informed information to the user, or both problems such as medical diagnosis fall under this category, where people will be unwilling to accept the verdict of the program, if they can not follow its reasoning.

Ex:- Problems such as medical diagnosis.

8. Problem Classification

Actual problems are examined from the point of view , the task here is examine an input and decide which of a set of known classes.

Ex:- Problems such as medical diagnosis , engineering design.

à(Chapter 19)Memory Organization:

Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world. Memory is also basis for learning.

Memory is used in every day common sense reasoning. Psychologically AI seeks to address these issues.

Psychological studies suggest several distinctions in Human memory. One distinction is between Short Term Memory (STM) and Long Term Memory (LTM).

LTM is often divided into episodic memory and semantic memory. Episodic memory contains information about past, personal experiences. Semantic memory on the other hand contains facts like “Bird Fly”. These facts are no longer connected with personal experiences.

Models for episodic memory grew out of research on scripts.

Recall that a script is a stereotyped sequence of events .

The components of a script include:

Entry Conditions

-- these must be satisfied before events in the script can occur.

Results

-- Conditions that will be true after events in script occur.

Props

-- Slots representing objects involved in events.

Roles

-- Persons involved in the events.

Track

-- Variations on the script. Different tracks may share components of the same script.

Scenes

-- The sequence of events that occur.

Ex:Scripts are useful in describing certain situations such as robbing a bank.

 This might involve:

• Getting a gun.
• Hold up a bank.
• Escape with the money.

Here the Props might be

• Gun, G.
• Loot, L.
• Bag, B
• Get away car, C.

The Roles might be:

• Robber, S.
• Cashier, M.
• Bank Manager, O.
• Policeman, P.

The Entry Conditions might be:

• S is poor.
• S is destitute.

The Results might be:

• S has more money.
• O is angry.
• M is in a state of shock.
• P is shot.

Usually three distinct Memory organizations packets (MOPS) encode knowledge about an even sequence.

(i)One MOP represents the Physical sequence of events.

(ii)Another MOP represents the set of social events that takes place.

(iii)Third MOP revolves around the goals of the person in the particular episode.

MOP’s organize scenes, and they themselves are further organized into higher level MOP’s.

For example, the MOP for visiting the office of a professional may contain a sequence of obstruct general scenes, such as

(i)                talking to an assistant

(ii)              waiting and meeting.

 High level MOP’s contain no actual memories. New MOP’s are created upon the failure of expectations. With MOP’s memory is both a constructive and reconstructive process.

It is constructive because new experiences create new memory structures. It is reconstructive because even if the details of a particular episode are lost, the MOP provides information about what was likely to have happened. The ability to do this kind of reconstruction is an important facture of Human Memory.

There are several MoP based computer programs. CYRUS program that contains episodes taken from the life of a particular individual. CYRUS can answer questions that require significant amounts of memory reconstruction.

àWhat does Fuzzy Logic mean?

Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. 

Fuzzy logic may be applied to many fields, including control systems, neural networks and artificial intelligence (AI).

àCase Based Reasoning

CBR is a method of reusing information of existing design cases for new designs.Case-based reasoning (CBR) research in artificial intelligence  also Problem Solving and Reasoning: Case-based) is concerned with the use of remembered prior experiences in analyzing new situations and solving problems

 The distinguishing characteristic of case-based reasoning is that prior experiences are stored as distinct histories or ‘cases,’