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Knowledge Representation

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Presentation on theme: "Knowledge Representation"— Presentation transcript:

1 Knowledge Representation
Peggy Israel Doerschuk Knowledge Rep

2 Requirements adequately reflect the types of knowledge needed
Knowledge Rep Requirements adequately reflect the types of knowledge needed allow new knowledge to be added and existing knowledge to be updated permit the derivation of new knowledge promote efficient processing of the information Copyright P. Doerschuk

3 Common representation schemes
Knowledge Rep Common representation schemes Logical representation predicate logic, propositional logic Procedural representation hard-coded sequential programs production systems network representation graph representation - semantic nets, conceptual dependencies, conceptual graphs Copyright P. Doerschuk

4 Common representation schemes cont.
Knowledge Rep Common representation schemes cont. relational representation relational databases knowledge represented by tuples or records languages like Structured Query Language (SQL) used to manipulate data hierarchical databases allow links between related groups of data Copyright P. Doerschuk

5 Common schemes cont. structured representation
Knowledge Rep Common schemes cont. structured representation frames, scripts, object databases, object-oriented programming languages knowledge is inheritable groups similar objects together compact representation allows reasoning at different levels of abstraction Copyright P. Doerschuk

6 Semantic Networks (Quillian)
Knowledge Rep Semantic Networks (Quillian) Models human information storage and retrieval association of concepts hierarchical organization - info is stored at its most abstract level canary is a type of bird; canary is yellow and can fly flying is stored with bird traits specific to canary (yellow) are stored with canary Copyright P. Doerschuk

7 Semantic Networks cont.
Knowledge Rep Semantic Networks cont. consists of nodes that represent an object, concept or event and arcs that represent a relationship between two nodes nodes are represented as rectangles or circles arcs are represented as directed arrows Examples: p. 202 of Luger, p. 65 of Bigus, other examples in Rich strength: inferencing via links, inheritance, flexibility weakness: too unconstrained Copyright P. Doerschuk

8 Conceptual Dependency
Knowledge Rep Conceptual Dependency Roger Schank (1974) models the deep semantic structure of natural language uses primitive conceptualizations to represent meaning primitives define conceptual dependency relationships conceptual dependency relationships are conceptual syntax rules used to construct internal representation of English sentence p of Luger Copyright P. Doerschuk

9 Scripts (Schank and Abelson)
Knowledge Rep Scripts (Schank and Abelson) used to represent common sequences of events contains background information and a collection of slots used to describe the scenes scenes are grouped into different tracks, depending on the particular situation scripts are limited to common scenes and can't be used for novel situations Copyright P. Doerschuk

10 Knowledge Rep Components of a script Entry conditions - must be true for script to be entered results - true when script is exited props roles scenes ex: Fig 6.11 Copyright P. Doerschuk

11 Knowledge Rep Frames consists of a collection of slots (attributes) and fillers (values) associated with the object of the frame slots can contain descriptive information (data), procedural information (functions), and pointer information (references to other frames) supports inheritance and inferencing frames are often linked to show has-a and is-a relationships example p. 63 of Bigus, Fig 6.12 of Luger, other examples in Rich frames can be represented as objects in OOP Copyright P. Doerschuk

12 Frames cont. Let complex object be represented by a single frame
Knowledge Rep Frames cont. Let complex object be represented by a single frame good for representing classes, inheritance, default values Copyright P. Doerschuk

13 Conceptual graphs John Sowa (1984)
Knowledge Rep Conceptual graphs John Sowa (1984) two types of nodes in the graph concepts (concrete or abstract)- boxes relations - ellipses arcs connect concepts to relations each concept box has the name of the type and the individual, separated by : markers are used to identify individuals # followed by number generic marker * marks unspecified individual Ex: Fig Copyright P. Doerschuk

14 Operations on conceptual graphs
Knowledge Rep Operations on conceptual graphs create a new graph by either specializing or generalizing an existing graph copy restrict - replace concept node with specialization generic marker replaced by individual marker type label replaced by subtype join simplify Fig 6.22 Copyright P. Doerschuk

15 Propositional nodes in conceptual graphs
Knowledge Rep Propositional nodes in conceptual graphs Propositional concepts are indicated as a box that contains another conceptual graph represent modal logics (various ways propositions are entertained - believed, asserted as true, false, possible, probable, etc.) ex: Tom believes that Jane lines pizza. Fig 6.24, 6.25 Copyright P. Doerschuk

16 Subsumpition Architecture
Knowledge Rep Subsumpition Architecture Rodney Brooks (1991) - intelligent behavior emerges from the interactions of architectures of organized simpler behaviors subsumption architecture used for robot control collection of task-handling behaviors each behavior accomplished by a finite state machine that maps perceptions to actions Copyright P. Doerschuk

17 Three-layered subsumption architecture
Knowledge Rep Three-layered subsumption architecture Each layer has a network of FSMs FSMs run asynchronously, sending and receiving messages no central control; each FSM is driven by the messages it receives Fig 6.26 Copyright P. Doerschuk

18 Limitations of subsumption architecture
Knowledge Rep Limitations of subsumption architecture Myopic - each level sees only local info no model of the complete environment means no ability to determine globally acceptable actions no learning can it scale to very large, complex systems? Copyright P. Doerschuk

19 Agent-Based and Distributed Problem Solving
Knowledge Rep Agent-Based and Distributed Problem Solving Characteristics of intelligent agent system: Situated - interacts with its environment autonomous - acts independently flexible - both responsive and proactive (goal directed) social - interacts with other agents communicate bid for subtasks cooperate, coordinate Copyright P. Doerschuk

20 Multi-agent problem solving
Knowledge Rep Multi-agent problem solving Problems are solved by multiple agents cooperating together, dividing and sharing knowledge of the problem each agent has incomplete info no global controller knowledge is decentralized reasoning processes are often asynchronous Copyright P. Doerschuk

21 Applications for agent-based problem solving
Knowledge Rep Applications for agent-based problem solving Manufacturing - modeled as hierarchy of work areas automated control - transportation systems, air traffic control, etc. telecommunications - network control, transmission and switching, etc. transportation systems information management - info filtering, gathering on the internet, etc. ecommerce - portfolio management, etc. interactive games Copyright P. Doerschuk

22 Knowledge Information Interchange (KIF)
Knowledge Rep Knowledge Information Interchange (KIF) Results from efforts of Defense Advanced Research Projects Agency Knowledge Sharing Environment workgroup Designed to provide a common format for exchanging knowledge between agents based on predicate logic, syntax similar to LISP supports definition of objects, functions, relations, rules, and metaknowledge ( knowledge about knowledge) Copyright P. Doerschuk

23 Knowledge Information Interchange cont
Knowledge Rep Knowledge Information Interchange cont a KIF knowledge base is a collection of forms A form is either a sentence, a rule, or a definition Copyright P. Doerschuk

24 Knowledge Information Interchange cont.
Knowledge Rep Knowledge Information Interchange cont. Variables individual variables begin with ?, sequence variables begin expressions terms - objects; sentences - facts; definitions - constants; rules - inferencing steps (=> (EventName “AGENT:STARTING”)(SetIdentifiedIntervalAlarm “NETSCAPE” 20 “minutes”) If we get an AGENT:STARTING event, start an alarm called NETSCAPE to go off every 20 minutes. operators term, rule, sentence, definition operators constants numbers, characters, strings, objects, functions, relations, logical constants Copyright P. Doerschuk

25 Building a Knowledge Base
Knowledge Rep Building a Knowledge Base The symbolic approach: Knowledge engineer gathers knowledge from domain expert(s) and represents it in a form used by the reasoning system expert must represent knowledge explicitly knowledge acquisition bottleneck the subsymbolic approach: expert networks use neural network to learn to perform classification and prediction tasks knowledge is encoded in weights between neurons Copyright P. Doerschuk

26 Research areas in intelligent agents
Knowledge Rep Research areas in intelligent agents How to decompose problem, synthesize results interagent communication how to ensure agents act coherently coordination resolving conflicts between agents how to recognize, avoid chaotic behavior how to allocate and manage resources what are the best hardware, software platforms Copyright P. Doerschuk

27 Representing Uncertainty
Knowledge Rep Representing Uncertainty Use statistical theory probability of an event ranges from 0 to 1 unconditional probability P(heads) = 0.5 conditional probability is expressed as: P(H|E) probability of hypothesis H given evidence E Copyright P. Doerschuk

28 Representing Uncertainty cont.
Knowledge Rep Representing Uncertainty cont. Bayes’ theorem: P(Y|X) = P(X|Y)P(Y)/P(X) Bayesian network a directed acyclic graph each node represents a variable and a conditional probability table defining relationships between parent nodes uses probability to reason with uncertainty Copyright P. Doerschuk


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