Presentation on theme: "Forward Chaining Systems also known as Production Systems commonly used for Expert Systems knowledge based must consist of facts and rules (Horn clauses,"— Presentation transcript:
Forward Chaining Systems also known as Production Systems commonly used for Expert Systems knowledge based must consist of facts and rules (Horn clauses, extended with retraction) will describe the Rete Algorithm –representation of concepts as network or graph
Rete Algorithm create a network where nodes represent ground literals (predicates with concrete arguments) rules link antecedents to consequent rules can create new nodes start by activating nodes corresponding to facts each iteration, determine which rules can fire pick one and modify network run until quiescence produces all the consequences of the facts
Conflict Resolution What happens when 2 rules can fire that have opposite effects? e.g assert(P) and retract(P)? –assign numeric priorities to rules – highest wins –can retract antecedents of other rules
Implementations –ACT, SOAR – cognitive models, simulate retrieval from long-term/short-term memory, activation by association, activation decay –CLIPS – C-based developed at NASA –JESS – Java-based, popular Subsumption Architecture (Rodney Brooks) –intelligent behavior in robots can be produced in a decentralized way by a lot simple rules interacting –divide behaviors into lower-level basic survival behaviors that have higher priority, and higher-level goal-directed behaviors –example: 6-legged robot ants learning to walk
Description Logics Theorem: entailment FOL is undecidable theorem provers like resolution might take a long time to find a proof Goal: knowledge representation system in which inference is more efficient than FOL solution: restrict expressiveness (e.g. eliminate disjunction and negation) –remember: proofs with Horn-clauses are linear-time will describe several DL systems –see Ch. 1 of Handbook of Description Logics for overview, link posted on web focus on defining taxonomy of concepts currently popular for web applications (“Semantic Web”)
Family of DL languages described in Ch. 2 of Handbook of DL AL family ALCN: full existential quantification full negation full number restriction a person who has at most one child, or 3 or more children including a daughter
Inference subsumption: is C D? reduce to satisfiability –C∩ D ? – is the “bottom” or NULL concept, nothing is in this set classification: find all subset relationships in hierarchy tableau algorithms –make model by expanding and re-writing concept definitions, or detect failure PSPACE-complete for ALCN
Semantic Web OWL - Web Ontology –extend data in XML with inference rules (written in RDF) –SHOIQN semantics –for example, if web page A is annotated with Joe Smith Dr. Hank Walker TAMU then should be able to infer that Dr. Walker is a faculty member at TAMU
Implementations –old: CLASSIC, KL-ONE, LOOM –modern: Protege Applications –medical records SNOMED ontology – describes terms for symptoms, diseases, procedures, anatomy, etc. –Dublin Core for information retrieval/media archiving describes books, journals, thesis, authors, publishers, ISBN, revisions, affiliations, conferences...
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