Artificial Intelligence

Artificial Intelligence A.I. can be define as the artificial brain having capability of thinking and understanding. A.I. is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.

Introduction, or what is knowledge? Knowledge Knowledge can be defined as the body of facts and principles accumulated by human kind or the act ,fact or state of knowing is a theoretical or practical understanding of a subject The sum of what is currently known, and apparently knowledge is power.

In biological organisms, knowledge is likely stored as complex structure of interconnected Neurons. In computers, knowledge is stored as symbolic structure but in form of collections of magnetic spots & voltage states

Knowledge Sources Documented (books, manuals, etc.) Undocumented (in people's minds) From people, from machines Knowledge Acquisition from Databases Knowledge Acquisition Via the Internet

Knowledge Levels Shallow knowledge (surface) Deep knowledge Can implement a computerized representation that is deeper than shallow knowledge Special knowledge representation methods (semantic networks and frames) to allow the implementation of deeper-level reasoning (abstraction and analogy): important expert activity Represent objects and processes of the domain of expertise at this level Relationships among objects are important

Scope of Knowledge Knowledge acquisition is the extraction of knowledge from sources of expertise and its transfer to the knowledge base and sometimes to the inference engine Knowledge is a collection of specialized facts, procedures and judgment rules

Domain Expert Those who possess knowledge are called experts. Anyone can be considered a domain expert if he or she has deep knowledge (of both facts and rules) and strong practical experience in a particular domain. The area of the domain may be limited. In general, an expert is a skilful person who can do things other people cannot. knowledgeable and skilled person capable of solving problems in a specific area or domain. Has the greatest expertise in a given domain. This expertise is to be captured in the expert system. Therefore, the expert must be able to communicate his or her knowledge, be willing to participate in the expert system development and commit a substantial amount of time to the project. Most important player in the expert system development team.

Major Categories of Knowledge Declarative Knowledge Procedural Knowledge Meta knowledge

Declarative Knowledge Descriptive Representation of Knowledge Expressed in a factual statement Shallow Important in the initial stage of knowledge acquisition

Procedural Knowledge Considers the manner in which things work under different sets of circumstances Includes step-by-step sequences and how-to types of instructions May also include explanations Involves automatic response to stimuli May tell how to use declarative knowledge and how to make inferences

Descriptive knowledge relates to a specific object. Includes information about the meaning, roles, environment, resources, activities, associations and outcomes of the object Procedural knowledge relates to the procedures employed in the problem-solving process

Meta knowledge Knowledge about Knowledge Meta knowledge can be simply defined as knowledge about knowledge. Meta knowledge is knowledge about the use and control of domain knowledge in an expert system. In ES, Meta knowledge refers to knowledge about the operation of knowledge-based systems Its reasoning capabilities

What’s in the knowledge base? Facts about the specifics of the world Northwestern is a private university The first thing I did at the party was talk to John. Rules that describe ways to infer new facts from existing facts All triangles have three sides All elephants are grey Facts and rules are stated in a formal language Generally some form of logic.

Knowledge-based systems A major turning point occurred in the field of AI with realization that “in knowledge lies the power”. This realization led to the development of a new class of system: i.e. knowledge –based system. knowledge –based system get their power from the expert knowledge that has been coded into facts , rules & procedure.

Components of KBS The knowledge is stored in a knowledge base separated from the control & inferencing component . This makes it possible to add new knowledge or refine existing knowledge without recompiling the control and inferencing programs. Input output unit Inference control unit Knowledge base

Structure and characteristics AI programs : intelligen t problem solving tools KBSs AI programs with special program structure separated knowledge base ESs KBSs applied in a specific narrow field /57 AI programs Knowledge-based systems Expert systems

The Knowledge Hierarchy meta- knowledge knowledge information data noise large volume, low value, usually no meaning/ context lower volume, higher value, with context and associated meanings understanding of a domain, can be applied to solve problems knowledge on knowledge (e.g how/when to apply) may contain irrelevant items which obscure data management information systems knowledge- based systems databases, transaction systems

Different type of knowledge base system

What is Knowledge Engineering? the process of building an ES the effort in developing a large quantity of effective knowledge (i.e. the KB) the acquisition of knowledge from a human expert or other source (by a knowledge engineering) and its coding in the ES KE is important, because: performance of an ES is largely determined by the quantity & quality of knowledge in its KB

knowledge Engineering The process of building knowledge-based systems is called knowledge engineering (KE). It has a great deal in common with software engineering, and is related to many computer science domains such as artificial intelligence, databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic and cognitive science as the knowledge is produced by cognitive systems (mainly humans) and is structured by our understanding of how human reasoning or logic works.

Knowledge Engineering Art of bringing the principles and tools of AI research to bear on difficult applications problems requiring experts' knowledge for their solutions Technical issues of acquiring, representing and using knowledge appropriately to construct and explain lines-of-reasoning Art of building complex computer programs that represent and reason with knowledge of the world (Feigenbaum and McCorduck [1983])

The knowledge engineer someone who is capable of designing, building and testing an expert system. interviews the domain expert to find out how a particular problem is solved. establishes what reasoning methods the expert uses to handle facts and rules and decides how to represent them in the expert system. chooses some development software or an expert system shell, or looks at programming languages for encoding the knowledge. responsible for testing, revising and integrating the expert system into the workplace.

Knowledge Engineering Process of acquiring knowledge from experts and building knowledge base Narrow perspective Knowledge acquisition, representation, validation, inference, maintenance Broad perspective Process of developing and maintaining intelligent system

Views of knowledge engineering There are two main views to knowledge engineering: Transfer View – This is the traditional view. In this view, the assumption is to apply conventional knowledge engineering techniques to transfer human knowledge into artificial intelligence systems. Modeling View – This is the alternative view. In this view, the knowledge engineer attempts to model the knowledge and problem solving techniques of the domain expert into the artificial intelligence system.

Knowledge Engineering Process Activities Knowledge Acquisition Knowledge Validation Knowledge Representation Inferencing Explanation and Justification

Knowledge Engineering Process Knowledge validation (test cases) Knowledge Representation Knowledge Acquisition Encoding Inferencing Sources of knowledge (experts, others) Explanation justification Knowledge base

Knowledge Engineering in a Nutshell human expert knowledge engineer knowledge base (in ES) explicit knowledge dialog knowledge refinement

Phases of KE Various phases of KE specific for the development of a knowledge-based system: * Assessment of the problem * Acquisition and structuring of related information, knowledge and specific preferences * Development of a knowledge-based system shell/structure * Implementation of the structured knowledge into knowledge-bases * Testing and validation of the inserted knowledge * Integration and maintenance of the system * Revision and evaluation of the system."

Knowledge Engineering Principles Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required. Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately. Knowledge engineers recognize that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge. Knowledge engineers recognize that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims. Knowledge engineers use structured methods to increase the efficiency of the acquisition process

The main players in the development team

Intelligent System A System can be constructed as a intelligence system if it has four major techniques of knowledge representation. 1.Logic The logic is a formal procedure because of which implications are created from the set of known facts. 2.Production Systems The production systems studies the new facts and the known facts and finds the desired conclusion. 3.Semantic networks It is a network of symbols that describe relationship between elements of knowledge 4.Frames These are the data structures which consists of expectations for a given situation.

“ Although knowledge representation is one of the central and in some ways most familiar concepts in AI, the most fundamental question about it What is it? has rarely been answered directly.”

What is a knowledge representation? A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. It is a set of ontological commitments, i.e., an answer to the question: In what terms should I think about the world? It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends. It is a medium for pragmatically efficient computation, i.e., the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences. It is a medium of human expression, i.e., a language in which we say things about the world.

Elements of a Representation Represented world: about what? Representing world: using what? Representing rules: how to map? Process that uses the representation: conventions and systems that use the representations resulting from above. Analog versus Symbolic

Understanding the roles and acknowledging their diversity has several useful consequences. First, each role requires something slightly different from a representation; each accordingly leads to an interesting and different set of properties we want a representation to have. Second, we believe the roles provide a framework useful for characterizing a wide variety of representations. We suggest that the fundamental "mindset" of a representation can be captured by understanding how it views each of the roles, and that doing so reveals essential similarities and differences. Third, we believe that some previous disagreements about representation are usefully disentangled when all five roles are given appropriate consideration. We demonstrate this by revisiting and dissecting the early arguments concerning frames and logic. Finally, we believe that viewing representations in this way has consequences for both research and practice. For research, this view provides one direct answer to a question of fundamental significance in the field. It also suggests adopting a broad perspective on what's important about a representation, and it makes the case that one significant part of the representation endeavor--capturing and representing the richness of the natural world--is receiving insufficient attention.

Terminology Two points of terminology will assist in our presentation. First, we use the term inference in a generic sense, to mean any way to get new expressions from old. We are only rarely talking about sound logical inference and when doing so refer to that explicitly. Second, to give them a single collective name, we refer to the familiar set of basic representation tools like logic, rules, frames, semantic nets, etc., as knowledge representation technologies.

We have argued that a knowledge representation plays five distinct roles, each important to the nature of representation and its basic tasks. Those roles create multiple, sometimes competing demands, requiring selective and intelligent tradeoff among the desired characteristics. Those five roles also aid in characterizing clearly the spirit of representations and representation technologies that have been developed. This view has consequences for both research and practice in the field. On the research front it argues for a conception of representation broader than the one often used, urging that all of the five aspects are essential representation issues. It argues that the ontological commitment a representation supplies is one of its most significant contributions; hence the commitment should be both substantial and carefully chosen. It also suggests that the fundamental task of representation is describing the natural world and claims that the field would advance furthest by taking this as its central preoccupation.

Different levels of knowledge representation Mental Image Written Text Magnetic Spots Binary Numbers Character Strings

How Knowledge Representation Works Intelligence requires knowledge Computational models of intelligence require models of knowledge Use formalisms to write down knowledge Expressive enough to capture human knowledge Precise enough to be understood by machines Separate knowledge from computational mechanisms that process it Important part of cognitive model is what the organism knows.

How knowledge representations are used in cognitive models Contents of KB is part of cognitive model Some models hypothesize multiple knowledge bases. Knowledge Base Inference Mechanism(s) Learning Mechanism(s) Examples, Statements Questions, requests Answers, analyses

Knowledge acquisition Knowledge acquisition includes the elicitation, collection, analysis, modelling and validation of knowledge for knowledge engineering and knowledge management projects

Issues in Knowledge Acquisition . Some of the most important issues in knowledge acquisition are as follows: Most knowledge is in the heads of experts Experts have vast amounts of knowledge Experts have a lot of tacit knowledge They don't know all that they know and use Tacit knowledge is hard (impossible) to describe Experts are very busy and valuable people Each expert doesn't know everything Knowledge has a "shelf life"

Requirements for KA Techniques Because of these issues, techniques are required which: Take experts off the job for short time periods Allow non-experts to understand the knowledge Focus on the essential knowledge Can capture tacit knowledge Allow knowledge to be collated from different experts Allow knowledge to be validated and maintained

KA Techniques … Many techniques have been developed to help elicit knowledge from an expert. These are referred to as knowledge elicitation or knowledge acquisition (KA) techniques. The term "KA techniques" is commonly used.The following list gives a brief introduction to the types of techniques used for acquiring, analysing and modelling knowledge: Protocol-generation techniques include various types of interviews (unstructured, semi-structured and structured), reporting techniques (such as self-report and shadowing) and observational techniques Protocol analysis techniques are used with transcripts of interviews or other text-based information to identify various types of knowledge, such as goals, decisions, relationships and attributes. This acts as a bridge between the use of protocol-based techniques and knowledge modelling techniques. Hierarchy-generation techniques, such as laddering , are used to build taxonomies or other hierarchical structures such as goal trees and decision networks.

KA Techniques … Matrix-based techniques involve the construction of grids indicating such things as problems encountered against possible solutions. Important types include the use of frames for representing the properties of concepts and the repertory grid technique used to elicit, rate, analyse and categorise the properties of concepts. Sorting techniques are used for capturing the way people compare and order concepts, and can lead to the revelation of knowledge about classes, properties and priorities. Limited-information and constrained-processing tasks are techniques that either limit the time and/or information available to the expert when performing tasks. For instance, the twenty-questions technique provides an efficient way of accessing the key information in a domain in a prioritised order. Diagram-based techniques include the generation and use of concept maps, state transition networks, event diagrams and process maps. The use of these is particularly important in capturing the "what, how, when, who and why" of tasks and events.

Knowledge Acquisition Methods: An Overview Manual Semiautomatic Automatic (Computer Aided)

Manual Methods Structured Around Interviews Process Interviewing Tracking the Reasoning Process Observing Manual methods: slow, expensive and sometimes inaccurate

Manual Methods of knowledge Acquisition Elicitation Knowledge base Documented knowledge Experts Coding Knowledge engineer

Semiautomatic Methods Support Experts Directly Help Knowledge Engineers

Expert-Driven Knowledge Acquisition Knowledge base Knowledge engineer Expert Coding Computer-aided (interactive) interviewing

Automatic Methods Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated) Induction Method.

Induction-Driven Knowledge Acquisition Knowledge base Case histories and examples Induction system

Knowledge Acquisition Difficulties Problems in Transferring Knowledge Expressing Knowledge Transfer to a Machine Number of Participants Structuring Knowledge

Experts may lack time or not cooperate Testing and refining knowledge is complicated Poorly defined methods for knowledge elicitation System builders may collect knowledge from one source, but the relevant knowledge may be scattered across several sources May collect documented knowledge rather than use experts The knowledge collected may be incomplete Difficult to recognize specific knowledge when mixed with irrelevant data Other Reasons

Experts may change their behavior when observed and/or interviewed Problematic interpersonal communication between the knowledge engineer and the expert Critical The ability and personality of the knowledge engineer Must develop a positive relationship with the expert The knowledge engineer must create the right impression Computer-aided knowledge acquisition tools Extensive integration of the acquisition efforts

Advantages of KBSs and ESs make up for shortage of experts, spread expert’ knowledge on available price field of interest’ changes are well-tracked increase expert’ ability and efficiency preserve know-how can be developed systems unrealizabled with tradicional technology (Buck Rogers) self-consistents in advising, equable in performance are available permanently able to work even with partial, non-complete data able to give expanation /57

Disadvantages of KBSs and ESs their knowledge is from a narrow field, don’t know the limits the answers are not always correct (advices have to be analysed !) don’t have common sence ( greatest restriction)  all of the self-evident checking have to be defined ( many exceptions  increase the size of KB and the running time ) /57

Role V: A KR is a Medium of Human Expression Finally, knowledge representations are also the means by which we express things about the world, the medium of expression and communication in which we tell the machine (and perhaps one another) about the world. This role for representations is inevitable so long as we need to tell the machine (or other people) about the world, and so long as we do so by creating and communicating representations. (5) The fifth role for knowledge representations is thus as a medium of expression and communication for use by us.

Role IV: A KR is a Medium for Efficient Computation From a purely mechanistic view, reasoning in machines (and somewhat more debatably, in people) is a computational process. Simply put, to use a representation we must compute with it. As a result, questions about computational efficiency are inevitably central to the notion of representation.

Role III: A KR is a Fragmentary Theory Of Intelligent Reasoning The third role for a representation is as a fragmentary theory of intelligent reasoning. This role comes about because the initial conception of a representation is typically motivated by some insight indicating how people reason intelligently, or by some belief about what it means to reason intelligently at all. The theory is fragmentary in two distinct senses: (i) the representation typically incorporates only part of the insight or belief that motivated it, and (ii) that insight or belief is in turn only a part of the complex and multi-faceted phenomenon of intelligent reasoning. A representation's theory of intelligent reasoning is often implicit, but can be made more evident by examining its three components: (i) the representation's fundamental conception of intelligent inference; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends.

Role II: A KR is a Set of Ontological Commitments If, as we have argued, all representations are imperfect approximations to reality, each approximation attending to some things and ignoring others, then in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world. That is, selecting a representation means making a set of ontological commitments. (2) The commitments are in effect a strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus, at the expense of blurring other parts.

Role I: A KR is a Surrogate Any intelligent entity that wishes to reason about its world encounters an important, inescapable fact: reasoning is a process that goes on internally, while most things it wishes to reason about exist only externally. A program (or person) engaged in planning the assembly of a bicycle, for instance, may have to reason about entities like wheels, chains, sprockets, handle bars, etc., yet such things exist only in the external world. This unavoidable dichotomy is a fundamental rationale and role for a representation: it functions as a surrogate inside the reasoner, a stand-in for the things that exist in the world. Operations on and with representations substitute for operations on the real thing, i.e., substitute for direct interaction with the world. In this view reasoning itself is in part a surrogate for action in the world, when we can not or do not (yet) want to take that action. (1)

Artificial Intelligence

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