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OUTLINE
YuitaArumSari,S.Kom,M.Kom 2
Tree Graph La,ce
Forward&BackwardChaining
Logic,Syllogism,
ModusPonens
ShallowandCasual
Reasoning
OtherInferenceMethod
YuitaArumSari,S.Kom,M.Kom3
Tree
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
v A tree is a hierarchical data structure consisting of: v Nodes – store information v Branches – connect the nodes
v The top node is the root, occupying the highest hierarchy. v The leaves are at the bottom, occupying the lowest hierarchy. v Every node, except the root, has exactly one parent. v Every node may give rise to zero or more child nodes. v A binary tree restricts the number of children per node to a
maximum of two. v Degenerate trees have only a single pathway from root to its
one leaf.
YuitaArumSari,S.Kom,M.Kom5
Graph
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Ø Graphs are sometimes called a network or net. Ø A graph can have zero or more links between nodes – there is
no distinction between parent and child. Ø Sometimes links have weights – weighted graph; or, arrows –
directed graph. Ø Simple graphs have no loops – links that come back onto the
node itself. Ø A circuit (cycle) is a path through the graph beginning and
ending with the same node.
Ø Acyclic graphs have no cycles.
Ø Connected graphs have links to all the nodes.
Ø Digraphs are graphs with directed links.
Ø Lattice is a directed acyclic graph.
YuitaArumSari,S.Kom,M.Kom7
MakingDecision
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Trees/la,cesareusefulforclassifyingobjectsinahierarchicalnature.
Trees/la,cesareusefulformakingdecisions.
Werefertotrees/la,cesasstructures.
DecisiontreesareusefulforrepresenFngandreasoningaboutknowledge.
YuitaArumSari,S.Kom,M.Kom8
BinaryDecisionTree
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
EveryquesFontakesusdownonelevelinthetree.
• Allleaveswillbeanswers.• AllinternalnodesarequesFons.• Therewillbeamaximumof2NanswersforNquesFons.
AbinarydecisiontreehavingNnodes:
Decisiontreescanbeselflearning.
DecisiontreescanbetranslatedintoproducFonrules.
YuitaArumSari,S.Kom,M.Kom10
StateandProblemSpaces
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Astatespacecanbeusedtodefineanobject’sbehavior.
DifferentstatesrefertocharacterisFcsthatdefinethestatusoftheobject.
AstatespaceshowsthetransiFonsanobjectcanmakeingoingfromonestatetoanother.
YuitaArumSari,S.Kom,M.Kom11
StateandProblemSpaces
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
AFSMisadiagramdescribingthefinitenumberofstatesofamachine.
AtanyoneFme,themachineisinoneparFcularstate.
Themachineacceptsinputandprogressestothenextstate.
FSMsareoYenusedincompilersandvaliditycheckingprograms.
YuitaArumSari,S.Kom,M.Kom12
UsingFSMtoSolveProblem
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Characterizingill-structuredproblems–one
havinguncertainFes.
Well-formedproblems:
• Explicitproblem,goal,andoperaFonsareknown
• DeterminisFc–wearesureofthenextstatewhenanoperatorisappliedtoastate.
• Theproblemspaceisbounded.• Thestatesarediscrete.
YuitaArumSari,S.Kom,M.Kom13
StateDiagramExample
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
State Diagram for a Soft Drink Vending Machine Accepting Quarters (Q) and Nickels (N)
YuitaArumSari,S.Kom,M.Kom14
AND-ORTreeandGoals
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Ø 1990s, PROLOG was used for commercial applications in business and industry.
Ø PROLOG uses backward chaining to divide problems into smaller problems and then solves them.
Ø AND-OR trees also use backward chaining.
Ø AND-OR-NOT lattices use logic gates to describe problems.
YuitaArumSari,S.Kom,M.Kom15
TypesofLogic
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
§ Deduction – reasoning where conclusions must follow from premises
§ Induction – inference is from the specific case to the general
§ Intuition – no proven theory
§ Heuristics – rules of thumb based on experience
§ Generate and test – trial and error § Abduction – reasoning back from a true condition to the
premises that may have caused the condition § Default – absence of specific knowledge § Autoepistemic – self-knowledge § Nonmonotonic – previous knowledge § Analogy – inferring conclusions based on similarities with other
situations
YuitaArumSari,S.Kom,M.Kom16
DeducWveLogic
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Argument–groupofstatementswherethelastisjusFfiedonthebasisofthepreviousones
DeducFvelogiccandeterminethevalidityofanargument.
Syllogism–hastwopremisesandoneconclusion
DeducFveargument–conclusionsreachedbyfollowingtruepremisesmustthemselvesbetrue
YuitaArumSari,S.Kom,M.Kom17
SyllogismvsRules
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Syllogism: All basketball players are tall. Jason is a basketball player. � Jason is tall.
IF-THEN rule:
IF All basketball players are tall and Jason is a basketball player THEN Jason is tall.
YuitaArumSari,S.Kom,M.Kom18
CategoricalSyllogism
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Premises and conclusions are defined using categorical statements of the form:
YuitaArumSari,S.Kom,M.Kom19
CategoricalSyllogism
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom20
RuleofInference
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Ø Venn diagrams are insufficient for complex arguments. Ø Syllogisms address only a small portion of the possible logical
statements. Ø Propositional logic offers another means of describing arguments.
1. If a class is empty, it is shaded. 2. Universal statements, A and E are always drawn before
particular ones. 3. If a class has at least one member, mark it with an *. 4. If a statement does not specify in which of two adjacent
classes an object exists, place an * on the line between the classes.
5. If an area has been shaded, not * can be put in it.
Proving the validity of syllogistic arguments using Venn Diagram
YuitaArumSari,S.Kom,M.Kom21
DirectReasoning-ModusPonens
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Modusponensisnecessarybecauseitshowsabasicrule-basedexpertsystems
YuitaArumSari,S.Kom,M.Kom23
TheModusMeanings
ThecondiFonalanditsVariant
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom24
LimitaWonsofPreposiWonalLogic
Ifanargumentisinvalid,itshouldbeinterpretedassuch–thattheconclusionisnecessarilyincorrect.
Anargumentmaybeinvalidbecauseitispoorlyconcocted.
AnargumentmaynotbeprovableusingproposiFonallogic,butmaybeprovableusingpredicatelogic.
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom25
ShallowandCausalReasoning
ExperienFalknowledgeisbasedonexperience.
Inshallowreasoning,thereisli`le/nocausalchainofcauseandeffectfromoneruletoanother.
Advantageofshallowreasoningiseaseofprogramming.
Framesareusedforcausal/deepreasoning.
Causalreasoningcanbeusedtoconstructamodelthatbehavesliketherealsystem.
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom26
Chaining
Chain–agroupofmulFpleinferencesthatconnectaproblemwithitssoluFon
Achainthatissearched/traversedfromaproblemtoitssoluFoniscalledaforwardchain.
Achaintraversedfromahypothesisbacktothefactsthatsupportthehypothesisisabackwardchain.
Problemwithbackwardchainingisfindachainlinkingtheevidencetothehypothesis.
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom27
CausalForwardChaining
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom28
SomeCharacterisWcFCandBC
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
YuitaArumSari,S.Kom,M.Kom29
SomeCharacterisWcFCandBC
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
Analogy–relaFngoldsituaFons(asaguide)tonewones.
Generate-and-Test–generaFonofalikelysoluFonthentesttoseeifproposedmeetsallrequirements.
AbducFon–FallacyoftheConverse
NonmonotonicReasoning–theoremsmaynotincreaseasthenumberofaxiomsincrease.
YuitaArumSari,S.Kom,M.Kom31
Metaknowledge
ExpertSystems:PrinciplesandProgramming,FourthEdi:on
v The Markov decision process (MDP) is a good application to path planning.
v In the real world, there is always uncertainty, and pure logic is not a good guide when there is uncertainty.
v A MDP is more realistic in the cases where there is partial or hidden information about the state and parameters, and the need for planning.
YuitaArumSari,S.Kom,M.Kom32
REFERENCES
• Joseph C. Giarratano, Gary D. Riley, Expert Systems: Principles and Programming, Published November 14th 2004 by Course Technology Inc.