1. Knowledge Representation Peggy Israel Doerschuk Knowledge Rep

2. 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

3. 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

4. Knowledge Rep Common representation schemescont. • 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

5. 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

6. 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

7. 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

8. 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. 206-210 of Luger

9. 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

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

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

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

13. 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 6.15-6.20

14. 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

15. 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

16. 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

17. 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

18. 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?

19. 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

20. 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

21. 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

22. 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)

23. 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

24. Knowledge Rep Knowledge Information Interchange cont. • Variables • individual variables begin with ?, sequence variables begin with @ • 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

25. 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

26. 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

27. 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

28. 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