Expert Systems

Expert System Functionality replace human expert decision making when not available assist human expert when integrating various decisions provides an ES user with –an appropriate hypothesis – methodology for knowledge storage and reuse border field to Knowledge Based Systems, Knowledge Management knowledge intensive × connectionist expert system – software systems simulating expert-like decision making while keeping knowledge separate from the reasoning mechanism

Expert Systems Classification Unlike classical problem solver (GPS, Theorist) Expert Systems are weak, less general, very case specific Exert systems classification : –Interpretation –Prediction –Diagnostic –Design & Configuration –Planning –Monitoring –Repair & Debugging –Instruction –Control

Underlying Philosophy knowledge representation –production rules –logic –semantic networks –frames, scripts, objects reasoning mechanism –knowledge-oriented reasoning –model-based reasoning –case-based reasonig

inference engine world model knowledge base user Expert System Architecture knowledge base editor preceptors explanation subsystem explanation subsystem

Rule-Based System knowledge in the form of if condition then effect (production) rules reasoning algorithm: (i)FR  detect(WM) (ii)R  select(FR) (iii)WM  apply R (iv)goto (i) conflicts in FR: –first, last recently used, minimal WM change, priorities incomplete WM – querying ES (art of logical and sensible querying) examples – CLIPS (OPS/5), Prolog

Rule-Based System Example here  fine not here  absent absent and not seen  at home absent and seen  in the building in the building  fine at home and not holiday  sick here and holiday  sick not here, in the building  fine not here, not holiday  sick ? here  no ? seen  no ? holiday  no sick ? here  yes fine ? here  yes ? holiday  yes sick

Data-driven × Goal-driven hereseenholiday absent building home fine sick data driven goal driven

Data-driven × Goal-driven goal driven ( backward chaining ) ~ blood diagnostic, theorem proving –limited number of goal hypothesis –data shall be acquired, complicated data about the object –less operators to start with at the goal rather than at the data data driven ( forward chaining ) ~ configuration, interpretation, –reasonable set of input data –data are given at the initial state –huge set of possible hypothesis taxonomy of rules, meta-rules, priorities, …

Knowledge Representation in ES Shallow Knowledge Models – rules, frames, logic, networks – first generation expert systems Deep Knowledge Models – describes complete systems causality – second generation expert systems Case Knowledge Models – specifies precedent in past decision making

Model Based Reasoning Sometimes it is either impossible or imprecise to describe the domain in terms of rules … Here we use a predictive computational model of the domain object in order to represent more theoretical deep knowledge model Model is based either on –quantitative reasoning (differential equations, …) –qualitative reasoning (emphasizes some properties while ignoring other) Very much used for model diagnosis and intelligent tutoring

Qualitative Reasoning Qualitative Reasoning is based on symbolic computation aimed at modeling of behavior of physical systems –commonsense inference mechanisms –partial, incomplete or uncertain information –simple, tractable computation –declarative knowledge QR Techniques: –Constrain based – Qualitative Simulation QSIM –Component based – Envision –Process based – QPT (Qualitative Process Theory)

Case Based Reasoning part of the machine learning lecture Algorithms: –problem attributes description –retrieval of previous case –solution modification –testing new solution –repairing failure or inclusion into the plan library Utilized widely in law domain (Judge)

Uncertainty in Expert Systems from correct premises and correct sound rules  correct conclusions but sometimes we have to manage uncertain information, encode uncertain pieces of knowledge, model parallel firing of inference rules, tackle ambiguity There is a number of various models of uncertain reasoning: – Bayesian Reasoning – classical statistical approach – Dempster-Shafer Theory of Evidence – Stanford Certainty Algebra – MYCIN

Bayesian Reasoning ……. given that a and b are independent ……. given that a depends on b - prior probability (unconditional) … p(hypothesis) - posterior probability (conditional)… p(hypothesis|evidence) Prospector, Dice examples P(e)P(h) P(e|h)

Bayesian Reasoning – cont’ we introduce Odds - O(h) we introduce sufficiency measure we introduce join Odds : e3e3 he2e2 e1e1

Stanford Certainty Algebra heuristic (expert given) approach for reasoning with uncertainty let us introduce – measure of belief MB(h|e) – measure of disbelief MD(h|e) – certainty factor CF(h|e) 1>MB(h|e)>0 if MD(h|e)=0 or 1>MD(h|e)>0 if MB(h|e)=0 CF(h|e) = MB(h|e)-MD(h|e) if P(h|e) = 1 otherwise

SCA characteristics: –certainly true – P(h|e)=1 => MB=1, MD=0, CF=1 –certainly false – P( |e)=1 => MB=0, MD=1, CF=-1 –lack of evidence – P(h|e)= P(h) => MB=0, MD=0, CF=0 Combination of evidence: – CF(e 1 and e 2 ) = min(CF(e 1 ),CF(e 2 )) – CF(e 1 or e 2 ) = max(CF(e 1 ),CF(e 2 )) Implication: if e then h – CF(h,e) = CF(e).CF(h,E), (where CF(h,E) is for CF(e)=1 ) Stanford Certainty Algebra – cont’

if the stain of the organism is gram positive and the morphology of the organism is coccus and the growth conformation of the organism is chains then there is suggestive evidence (CF(h,E)=0.7) that the identity of the organism is streptococcus CF(e)=CF( ) CF(e)= min[CF(e 1 ),CF(e 2 ),CF(e 3 )] CF(e)=min[0.5,0.6,0.3] CF(e)=0.3 CF(h,e)=CF(e),CF(h,E) CF(h,e)=0.3 × 0.7 CF(h,e)=0.21

Dempster-Shafer Theory of Evidence frame of discernment – a space of possible events/answers/options  = {airliner,bomber,fighter}  = {red,green,blue,orange,yellow}  = {barn,grass,person,cow,car}  is exclusive, probability of the right answer in  is 1 basic probability assignment m(E), degree of belief of evidence example – what was detected is 70% hostile (the only information) m( {b,f} ) =0.7, m(  ) =0.3 m( {b} )=m( {f} )=m( {b,a} )=m( {f, a})=0

DST – Combining of Evidence Dempster Rule of Combination – Orthogonal Sum m 1 ( {b,f} ) =0.7, m 1 (  ) =0.3 m 2 ( {b} ) =0.9, m 2 (  ) =0.1 m 1  m 2 ( {b,f} )=m 1 ( {b,f} )×m 2 (  )= 0.7 ×0.1=0.07 m 1  m 2 ( {b} )=m 1 ( {b,f} )×m 2 ( {b} )+m 1 (  )×m 2 ( {b} )= 0.7× ×0.9 = =0.97

DST – Total Belief/Plausibility in contrast to local belief in the set - m(E), let us introduce total belief set Bel(E), minimum belief based on given evidence in contrast there is plausibility – maximum plausible belief assigned to the set E Bel 1 ( {b,f} )=m 1 ( {b, f} )+m 1 ( {b} ) +m 1 ( {f} )= = 0.7 Bel 1  Bel 2 ( {b,f} )= m 1  m 2 ( {b, f} )+ m 1  m 2 ( {b} ) + m 1  m 2 ( {f} )= = 0.97

Fuzzy Logic Another way of handling incomplete knowledge Precision/vagueness is expressed by membership function to a set mF(20,adult)=0.6, mF(20,young)=0.4, mF(20,old)=0 adult pensioneryoung

Fuzzy Logic – cont’ Fuzzy Logic is not concerned how these distribution are created but how they are manipulated. There are many interpretation, similar to Stanford Certainty Algebra mF(20,adult and young)=0.4, mF(20,adult or young)=0.6 comparison to previous approaches: vaguenessrandomness possibilityprobability inexact reasoninguncertain reasoning fuzzy setsclassical approaches

Expert Systems in Practice MYCIN –example of medical expert system –old well known reference –great use of Stanford Certainty Algebra –problems with legal liability and knowledge acquisition Prospector –geological system –knowledge encoded in semantic networks –Bayesian model of uncertainty handling –saved much money

Expert Systems in Practice – cont’ XCON/R1 –classical rule-based system –configuration DEC computer systems –commercial application, well used, followed by XSEL, XSITE –failed operating after 1700 rules in the knowledge base FelExpert –rule-based, bayesian model, –taxonomised, used in a number of applications ICON –configuration expert system –uses proof planning structure of methods