Gerstner Laboratory

for Intelligent Decision Making and Control

Expert Systems I

Michal Pěchouček

Gerstner Laboratory for Intelligent Decision Making and Control

Gerstner Laboratory

for Intelligent Decision Making and Control

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

Gerstner Laboratory

for Intelligent Decision Making and Control

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

Gerstner Laboratory

for Intelligent Decision Making and Control

Underlying Philosophy

• knowledge representation – production rules– logic – semantic networks– frames, scripts, objects

• reasoning mechanism

– knowledge-oriented reasoning– model-based reasoning– case-based reasonig

Gerstner Laboratory

for Intelligent Decision Making and Control

inference engine

world model

knowledge base

user

Expert System Architecture

knowledge base editor

preceptors

explanation subsystem

explanation subsystem

Gerstner Laboratory

for Intelligent Decision Making and Control

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

Gerstner Laboratory

for Intelligent Decision Making and Control

Rule-Based System Example

here finenot here absentabsent and not seen at homeabsent and seen in the buildingin the building fineat home and not holiday sickhere and holiday sick

not here, in the building fine

not here, not holiday sick

? here no? seen no? holiday nosick

? here yesfine

? here yes? holiday yessick

Gerstner Laboratory

for Intelligent Decision Making and Control

Data-driven × Goal-driven

here seen holiday

absent

buildinghome

fine sick

data driven

goal driven

Gerstner Laboratory

for Intelligent Decision Making and Control

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, …

Gerstner Laboratory

for Intelligent Decision Making and Control

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

Gerstner Laboratory

for Intelligent Decision Making and Control

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

Gerstner Laboratory

for Intelligent Decision Making and Control

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)

Gerstner Laboratory

for Intelligent Decision Making and Control

QSIM – A Constraints Based Approach• Qualitative system is described by parameters, domains and

constraints (relations among parameters)• Qualitative simulation is thus only breath-first-search in the

space of possible combination of values of the parameters

• Qualitative behaviour is thus a path in the tree from the initial state to some leaf state

• The structure of the system model is given in the form of qualitative equation consisting of constraints: – arithmetic – add(A,B,C),mult(A,B,C)– derivative – der(height, velocity)– monotonicity – M+(wrinkle,age) M-

(hunger,consumption)

Gerstner Laboratory

for Intelligent Decision Making and Control

QSIM – A Constraints Based Approach• Qualitative State of each parameter is a couple:

{value,direction} where value can be either an interval or landmark value and direction may be inc (increasing), dec

(increasing) or std (steady)• Qualitative Reasoning Procedure:

(i) wm initial state(ii) succ find-successors of first(wm)(iii) succ filter(succ)(iv) wm wm – first(wm) + succ

• Filtering: pairwise consistency, redundancy, cycles, termination condition, logical direction of change, qualitative magnitude change

Gerstner Laboratory

for Intelligent Decision Making and Control

QSIM – A Pendulum Example

• system description: der(v,a) and der(s,v)• domains: a = {min,min,0,0,0,max,max}

v = {min,min,0,0,0,max,max}

s = {0,0,max,max}

aav

s

s 0,std +,inc +,inc +,incmax,st

d

v 0,std +,incmax,st

d+,dec 0,std

amax,st

d+,dec 0,std -,dec min,std

smax,st

d+,dec +,dec +,dec 0,std

v 0,std -,dec min,std -,inc 0,std

a min,std -,inc 0,std +,inc min,std

Gerstner Laboratory

for Intelligent Decision Making and Control

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)

Gerstner Laboratory

for Intelligent Decision Making and Control

Knowledge Evolution

• Strong Update - result of application of the knowledge extraction process on the set E S.

• Weak Update - relevant bits of the inference knowledge-base re-computation

strong update

weak update

inferencerules exceptionI L P

'strong' update

weak update

decisiongraph EBGFilters