1. 1 CMSC 671 Fall 2003 Class #16 – Wednesday, October 22

2. 2 Today’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 reasoning

3. 3 Knowledge Representation and Reasoning Chapters 10.1-10.3, 10.6, 10.9; also includes some material from 13.1-13.2 and 14.7 Some material adopted from notes by Andreas Geyer-Schulz and Chuck Dyer

4. 4 Introduction 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 reasoning

5. 5 Forward-chaining production systems The notion of a “production system” was invented in 1943 by Post Used as the basis for many rule-based expert systems Used as a model of human cognition in psychology A production is a rule of the form: C1, C2, … Cn => A1 A2 …Am Left hand side (LHS)Right hand side (RHS) Condition which must hold before the rule can be applied Actions to be performed or conclusions to be drawn when the rule is applied

6. 6 Production systems: Basic components Rules -- Unordered set of user-defined “if-then” rules. –Form: if P 1 ...  P m then A 1,..., A n –The P i are facts that determine the conditions when a rule is applicable. –Actions can add or delete facts from the working memory. Working Memory -- A set of “facts” consisting of positive literals defining what’s known to be true about the world –Usually “flat tuples” like (location umbc baltimore) Inference Engine -- Procedure for inferring changes (additions and deletions) to working memory –Typically uses forward chaining to make inferences

7. 7 Typical CLIPS Rule (defrule determine-gas-level "" (working-state engine does-not-start) (rotation-state engine rotates) (not (repair ?)) => (if (not (yes-or-no-p “Gas in tank?")) then (assert (repair "Add gas.")))) (defrule normal-engine-state-conclusions "" (declare (salience 10)) (working-state engine normal) => (assert (repair "No repair needed.")) (assert (spark-state engine normal)) (assert (charge-state battery charged)) (assert (rotation-state engine rotates))) (defrule print-repair "" (declare (salience 10)) (repair ?item) => (printout t crlf crlf) (printout t "Suggested Repair:") (printout t crlf crlf) (format t " %s%n%n%n" ?item))

8. 8 Typical CLIPS facts (initial-fact) (working-state engine unsatisfactory) (charge-state battery charged) (rotation-state engine rotates) (repair "Clean the fuel line.") (engine (horsepower 250) (displacement 409) (manufacturer ford)) Facts in most production systems are basically flat tuples A simple extension supported by many is to allow simple templates using“slot-filler” pairs. (deftemplate engine (slot horsepower) (slot displacement) (slot manufacturer) (slot year)) Matching slots in a template is order insensitive, as in: (engine (year 1998) (horsepower ?x)) (engine (horsepower 250) (displacement 500) (year 1998))

9. 9 Basic Procedure While changes are made to Working Memory do: Match: Construct the Conflict Set -- the set of all possible (R, F) pairs such that R is one of the rules and F is a subset of facts in WM that unify with the antecedent (left-hand side) of R. Conflict Resolution: Select one pair from the Conflict Set for execution. Act: Execute the actions associated with the consequent (right-hand side) of R, after making the substitutions used during unification of the antecedent part with F.

10. 10 Rete Algorithm The Rete Algorithm (Greek for “net”) is the most widely used, efficient algorithm for the implementation of production systems. Developed by Charles Forgy at Carnegie Mellon University in 1979. –Charles L. Forgy, "Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem", Artificial Intelligence,19, pp 17-37, 1982. Rete is the only algorithm for production systems whose efficiency is asymptotically independent of the number of rules. The basis for a whole generation of fast expert system shells: OPS5, ART, CLIPS and Jess.

11. 11 Match Phase RULES (defrule R1 “rule one” (a ?x)(b ?x)(c ?y) => (assert (d ?x))) (defrule R2 “rule two” (a ?x)(b ?y)(d ?x) => (assert (e ?x))) (defrule R3 “rule three” ?fact <- (a ?x) (b ?x) (e ?x) => (remove ?fact)) WORKING MEMORY (a 1) (a 2) (b 2) (b 3) (b 4) (c 5)

12. 12 Conflict Resolution Strategy Components Refraction –A rule can only be used once with the same set of facts in WM. Whenever WM is modified, all rules can again be used. This strategy prevents a single rule and list of facts from being used repeatedly, resulting in an infinite loop of reasoning. Recency –Use rules that match the facts that were added most recently to WM, providing a kind of “focus of attention” strategy. Specificity –Use the most specific rule: if both R1 and R2 match, and R1’s LHS logically implies R2’s LHS, use R2. Explicit priorities –E.g., numeric salience attribute for rules

13. 13 An Application: R1 / XCON An expert systems developed by DEC for configuration. Problem: develop a single acceptable configuration of hardware components for a complete computer system based on partial customer specifications. Rules are used to determine –if an order is complete and, if not, adds necessary items, –the spatial relations (connectivity) of components Bottom-up, data-driven, forward-chaining deductive system for synthesizing a solution. –No backtracking needed --- constraints in rules are sufficient to directly construct a solution Rules always determine locally whether taking a particular action is globally consistent with acceptable overall performance on the task Had about 10,000 rules

14. 14 Example XCON rule Assign_Power_Supply_1 IF –Most current active context is assign-power-supply –An SBI module is in cabinet –Position of module in cabinet is known –Space is available in cabinet for a power supply for that position –No power supply is currently available –Voltage + frequency of components is known THEN – Find a power supply of that voltage and frequency and add it to order

15. 15 R1/XCON WM contains current configuration of components, e.g., space filled and unfilled in cabinets and backplane slots. Uses specialization conflict resolution strategy plus “context” markers in WM to control the firing of rules. Groups of rules are associated into separate “contexts” which define a particular sub-task that they are used for. First antecedent in each rule indicates the context where the rule is to be used. For example, the rule above is to be used in the context of “assigning a power supply.”

16. 16 A context changing rule To change contexts, there are rules such as the following Check_Voltage_And_Frequency_1 IF Most current active context is checking-voltage-and-frequency There is a component requiring one voltage or frequency There is another component requiring a different voltage or frequency THEN Enter context of fixing-voltage-or-frequency-mismatches

17. 17 Semantic 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.

18. 18 Nodes and Arcs Arcs define binary relationships that hold between objects denoted by the nodes. john5 Sue age mother mother(john,sue) age(john,5) wife(sue,max) age(max,34)... 34 age father Max wife husband age

19. 19 Semantic 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 level isa Robin Bird Animal RedRusty hasPart Wing

20. 20 Reification 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) give marybook32 john recipient giver object

21. 21 Individuals and Classes Many semantic networks distinguish –nodes representing individuals and those representing classes –the “subclass” relation from the “instance-of” relation subclass instance Robin Bird Animal RedRusty hasPart Wing instance Genus

22. 22 Link types

23. 23 Inference 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 inherited

24. 24 Conflicting inherited values

25. 25 Multiple 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.

26. 26 Nixon Diamond This was the classic example circa 1980. Person Republican Person Quaker instance subclass FALSE pacifist TRUE pacifist

27. 27 From 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.

28. 28 Facets 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.

29. 29

30. 30 Description 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 inference

31. 31 Abduction 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 reasoning

32. 32 Abduction Definition (Encyclopedia Britannica): reasoning that derives an explanatory hypothesis from a given set of facts –The inference result is a hypothesis that, if true, could explain the occurrence of the given facts Examples –Dendral, an expert system to construct 3D structure of chemical compounds Fact: mass spectrometer data of the compound and its chemical formula KB: chemistry, esp. strength of different types of bounds Reasoning: form a hypothetical 3D structure that satisfies the chemical formula, and that would most likely produce the given mass spectrum

33. 33 –Medical diagnosis Facts: symptoms, lab test results, and other observed findings (called manifestations) KB: causal associations between diseases and manifestations Reasoning: one or more diseases whose presence would causally explain the occurrence of the given manifestations –Many other reasoning processes (e.g., word sense disambiguation in natural language process, image understanding, criminal investigation) can also been seen as abductive reasoning Abduction examples (cont.)

34. 34 Comparing abduction, deduction, and induction Deduction: major premise: All balls in the box are black minor premise: These balls are from the box conclusion: These balls are black Abduction: rule: All balls in the box are black observation: These balls are black explanation: These balls are from the box Induction: case: These balls are from the box observation: These balls are black hypothesized rule: All ball in the box are black A => B A --------- B A => B B ------------- Possibly A Whenever A then B ------------- Possibly A => B Deduction reasons from causes to effects Abduction reasons from effects to causes Induction reasons from specific cases to general rules

35. 35 Characteristics of abductive reasoning “Conclusions” are hypotheses, not theorems (may be false even if rules and facts are true) –E.g., misdiagnosis in medicine There may be multiple plausible hypotheses –Given rules A => B and C => B, and fact B, both A and C are plausible hypotheses –Abduction is inherently uncertain –Hypotheses can be ranked by their plausibility (if it can be determined)

36. 36 Characteristics of abductive reasoning (cont.) Reasoning is often a hypothesize-and-test cycle –Hypothesize: Postulate possible hypotheses, any of which would explain the given facts (or at least most of the important facts) –Test: Test the plausibility of all or some of these hypotheses –One way to test a hypothesis H is to ask whether something that is currently unknown–but can be predicted from H–is actually true If we also know A => D and C => E, then ask if D and E are true If D is true and E is false, then hypothesis A becomes more plausible (support for A is increased; support for C is decreased)

37. 37 Characteristics of abductive reasoning (cont.) Reasoning is non-monotonic –That is, the plausibility of hypotheses can increase/decrease as new facts are collected –In contrast, deductive inference is monotonic: it never change a sentence’s truth value, once known –In abductive (and inductive) reasoning, some hypotheses may be discarded, and new ones formed, when new observations are made

38. 38 Sources of uncertainty Uncertain inputs –Missing data –Noisy data Uncertain knowledge –Multiple causes lead to multiple effects –Incomplete enumeration of conditions or effects –Incomplete knowledge of causality in the domain –Probabilistic/stochastic effects Uncertain outputs –Abduction and induction are inherently uncertain –Default reasoning, even in deductive fashion, is uncertain –Incomplete deductive inference may be uncertain  Probabilistic reasoning only gives probabilistic results (summarizes uncertainty from various sources)

39. 39 Decision making with uncertainty Rational behavior: –For each possible action, identify the possible outcomes –Compute the probability of each outcome –Compute the utility of each outcome –Compute the probability-weighted (expected) utility over possible outcomes for each action –Select the action with the highest expected utility (principle of Maximum Expected Utility)

40. 40 Bayesian reasoning Probability theory Bayesian inference –Use probability theory and information about independence –Reason diagnostically (from evidence (effects) to conclusions (causes)) or causally (from causes to effects) Bayesian networks –Compact representation of probability distribution over a set of propositional random variables –Take advantage of independence relationships

41. 41 Other uncertainty representations Default reasoning –Nonmonotonic logic: Allow the retraction of default beliefs if they prove to be false Rule-based methods –Certainty factors (Mycin): propagate simple models of belief through causal or diagnostic rules Evidential reasoning –Dempster-Shafer theory: Bel(P) is a measure of the evidence for P; Bel(  P) is a measure of the evidence against P; together they define a belief interval (lower and upper bounds on confidence) Fuzzy reasoning –Fuzzy sets: How well does an object satisfy a vague property? –Fuzzy logic: “How true” is a logical statement?

42. 42 Uncertainty tradeoffs Bayesian networks: Nice theoretical properties combined with efficient reasoning make BNs very popular; limited expressiveness, knowledge engineering challenges may limit uses Nonmonotonic logic: Represent commonsense reasoning, but can be computationally very expensive Certainty factors: Not semantically well founded Dempster-Shafer theory: Has nice formal properties, but can be computationally expensive, and intervals tend to grow towards [0,1] (not a very useful conclusion) Fuzzy reasoning: Semantics are unclear (fuzzy!), but has proved very useful for commercial applications