CMSC 671 Fall 2005Today’s topicsKnowledge Representation and ReasoningIntroductionSemantic NetworksNodes and ArcsSlide 7ReificationIndividuals and ClassesInference by InheritanceConflicting inherited valuesMultiple inheritanceFrom Semantic Nets to FramesFacetsSlide 17Description LogicsAbductionSlide 20Abduction examples (cont.)Comparing abduction, deduction, and inductionCharacteristics of abductive reasoningCharacteristics of abductive reasoning (cont.)Slide 25Sources of uncertaintyDecision making with uncertaintyBayesian reasoningOther uncertainty representationsUncertainty tradeoffs1CMSC 671CMSC 671Fall 2005Fall 2005Class #13 –Thursday, October 132Today’s topics•Approaches to knowledge representation•Deductive/logical methods–Forward-chaining production rule systems–Semantic networks–Frame-based systems–Description logics•Abductive/uncertain methods–What’s abduction?–Why do we need uncertainty?–Bayesian reasoning–Other methods: Default reasoning, rule-based methods, Dempster-Shafer theory, fuzzy reasoning3Knowledge Knowledge Representation and Representation and ReasoningReasoningChapters 10.1-10.3, 10.6, 10.9Some material adopted from notes by Andreas Geyer-Schulzand Chuck Dyer4Introduction•Real knowledge representation and reasoning systems come in several major varieties.•These differ in their intended use, expressivity, features,…•Some major families are–Logic programming languages–Theorem provers–Rule-based or production systems–Semantic networks–Frame-based representation languages–Databases (deductive, relational, object-oriented, etc.)–Constraint reasoning systems–Description logics–Bayesian networks–Evidential reasoning5Semantic Networks•A semantic network is a simple representation scheme that uses a graph of labeled nodes and labeled, directed arcs to encode knowledge.–Usually used to represent static, taxonomic, concept dictionaries•Semantic networks are typically used with a special set of accessing procedures that perform “reasoning”–e.g., inheritance of values and relationships•Semantic networks were very popular in the ‘60s and ‘70s but are less frequently used today.–Often much less expressive than other KR formalisms•The graphical depiction associated with a semantic network is a significant reason for their popularity.6Nodes and Arcs•Arcs define binary relationships that hold between objects denoted by the nodes.john 5Sueagemothermother(john,sue)age(john,5)wife(sue,max)age(max,34)...34agefatherMaxwifehusbandage7Semantic Networks•The ISA (is-a) or AKO (a-kind-of) relation is often used to link instances to classes, classes to superclasses•Some links (e.g. hasPart) are inherited along ISA paths.•The semantics of a semantic net can be relatively informal or very formal–often defined at the implementation levelisaisaisaisaRobinBirdAnimalRedRustyhasPartWing8Reification•Non-binary relationships can be represented by “turning the relationship into an object”•This is an example of what logicians call “reification”–reify v : consider an abstract concept to be real •We might want to represent the generic give event as a relation involving three things: a giver, a recipient and an object, give(john,mary,book32)givemary book32johnrecipientgiverobject9Individuals and Classes•Many semantic networks distinguish–nodes representing individuals and those representing classes–the “subclass” relation from the “instance-of” relationsubclasssubclassinstanceinstanceRobinBirdAnimalRedRustyhasPartWinginstanceGenus11Inference by Inheritance•One of the main kinds of reasoning done in a semantic net is the inheritance of values along the subclass and instance links.•Semantic networks differ in how they handle the case of inheriting multiple different values.–All possible values are inherited, or–Only the “lowest” value or values are inherited12Conflicting inherited values13Multiple inheritance•A node can have any number of superclasses that contain it, enabling a node to inherit properties from multiple “parent” nodes and their ancestors in the network. •These rules are often used to determine inheritance in such “tangled” networks where multiple inheritance is allowed:–If X<A<B and both A and B have property P, then X inherits A’s property.–If X<A and X<B but neither A<B nor B<Z, and A and B have property P with different and inconsistent values, then X does not inherit property P at all.15From Semantic Nets to Frames•Semantic networks morphed into Frame Representation Languages in the ‘70s and ‘80s.•A frame is a lot like the notion of an object in OOP, but has more meta-data.•A frame has a set of slots.•A slot represents a relation to another frame (or value).•A slot has one or more facets.•A facet represents some aspect of the relation.16Facets•A slot in a frame holds more than a value.•Other facets might include:–current fillers (e.g., values)–default fillers–minimum and maximum number of fillers–type restriction on fillers (usually expressed as another frame object)–attached procedures (if-needed, if-added, if-removed)–salience measure–attached constraints or axioms•In some systems, the slots themselves are instances of frames.1718Description Logics•Description logics provide a family of frame-like KR systems with a formal semantics.–E.g., KL-ONE, LOOM, Classic, …•An additional kind of inference done by these systems is automatic classification– finding the right place in a hierarchy of objects for a new description •Current systems take care to keep the languages simple, so that all inference can be done in polynomial time (in the number of objects)–ensuring tractability of inference19Abduction•Abduction is a reasoning process that tries to form plausible explanations for abnormal observations–Abduction is distinctly different from deduction and induction–Abduction is inherently uncertain•Uncertainty is an important issue in abductive reasoning•Some major formalisms for representing and reasoning about uncertainty–Mycin’s certainty factors (an early representative)–Probability theory (esp. Bayesian belief networks)–Dempster-Shafer theory–Fuzzy logic–Truth maintenance systems–Nonmonotonic reasoning20Abduction•Definition (Encyclopedia Britannica): reasoning that derives an explanatory hypothesis from a given set of facts–The inference result is a