Knowledge-Based Expert Systems

A computer program which, with its associated data, embodies organised knowledge concerning some specific area of human activity. Such a system is expected to perform competently, skilfully and in a cost-effective manner; it may be thought of as a computer program which mimics the performance of a human expert.

Reasoning StrategiesReasoning may be characterised as an attempt to combine elements of old information to form new information.

Reasoning strategies refer to the rather long sequences of individual small inferences organised so as to address a main goal or problem.

Reasoning strategies involve the representation of information and knowledge, the use of inference rules for manipulating that knowledge & information, and control mechanisms for making the variety of choices necessary in the search for solutions.

Prevent the hi-jacking of airliners

Prevent hi-jackers from boarding the airliners

Heuristic technique Algorithmic route

•Put passengers and luggage through a metal detector &•Search only those who set off the detector • Search those passengers that match a predetermined hi-jacker profile

(inc. passengers, flight crews & mechanics)

•Strip search every person with access to the airlines &

•Search all luggage

Heuristics or Rules of thumb

•There are recognised experts

•The experts are provably better than novices

•The tasks takes an expert a few minutes to a few hours (if it takes days - FORGET IT)

•The task is primarily cognitive

•The skill is routinely taught to novices

•The task requires no common sense

•The task domain is important: economically, financially or socially

Knowledge Based Systems & Applications

Knowledge EngineeringThe accumulation, codification and application of knowledge through the use of computer systems, specifically knowledge-based systems. In RBS involves identifying domain objects an relationships and converting them to If – Then rules.

Rule-based Systems

A rule-based system helps us to codify the problem-solving knowledge of the human expert.

It appears that experts typically express their knowledge as a set of situation-action rules.

RBS research should address the need to capture, represent, store, distribute, reason about and apply human knowledge electronically.

Hayes Roth, F. (1992). ‘Rule-Based Systems’ p.1426

Representation: Production Systems

•Production Systems are a modular knowledge representation scheme and are based on the notion of condition-action pairs, called production rules or just productions: "If this condition occurs, then do this action". •The utility of the production system formalism comes from the fact that the conditions in which each rule is applicable are made explicit and, in theory at least, the interactions between rules are minimised in the sense that the rules do not 'call' each other.

Representation: Production Systems

Conflict Resolution Rule Ordering

Context Limiting

Specificity

Refractoriness

Recency

Knowledge Engineering

The Good News Human

Expertise Artificial Expertise

Durability Perishable Permanent Transfer Difficult Easy Documentation Difficult Easy Consistency Unpredictable Consistent Cost High Affordable

The Bad News Human

Expertise Artificial Expertise

Creativity Yes None Adaptivity Yes Limited Input Sensory/

Symbolic Symbolic only

Focus Broad Narrow Common-sense Yes None

Rule-Based System ApplicationsProblem System Functions

Equipment maintenance Diagnose faults and recommend repairs

Component Selection Elicit requirements and match parts

Computer operation Analyse requirements; select and operate software

Product configuration Elicit preferences and identify parts that satisfy constraints

Troubleshooting Analyse situation, suggest treatments

Process control Spot problematic data and remedy irregularities

Quality assurance Assess task, propose practices, and enforce requirements

Reaction Systems

Perform some actionThe antecedent specifies conditions and the consequent specifies some action Add a new assertion Delete an existing assertion Execute a procedure

Planning

PLANNING

Definition

In AI, planning involves the generation of an action sequence or action program for an agent, such as a robot or a software system or a living artefact, that can alter its surroundings.

PLANNING

It is in the handling of novel situations or critical situations that we see the a substantial planning-related activities: examination of initial states, a collection of actions for transforming the initial states and a set of goals.

PLANNINGDefinition b A planning system performs the following functions: 

1. Choice The system can choose the best rule to apply next based on the best available heuristic information2. Application The system can apply the chosen rule to compute the new problem state that arises from its application.3. Detection The system can detect when a solution has been found

4. Abandonment

The system can abandon dead-ends and can direct its efforts to more fruitful avenues.

THE BLOCKS WORLD

The 'blocks world' was amongst the favourite topics in AI: there were Ph D theses, there were learned papers, conferences and so on during the late sixties and the early seventies.  The 'blocks world' is an imaginary two-dimensional world comprising: • A number of (usually distinctly labelled) square blocks;

• The square blocks can be arranged to form stacks;

• A table of unbounded size.

THE BLOCKS WORLDThe BLOCKS world states can be described by three kinds of literals  CLEAR (x)

There is no block on top of x

   

ON (x, y) Block x rests directly on block y

   

HOLDING (x)

A hand of an agent holding block x 

 

The initial state description for a completely known world consists of a

conjunction of these literals.

THE BLOCKS WORLD - EXAMPLE 2

Three operators: The operators are made up of the literals explained above and are shown below: 

Operator 1:

/* Move block x from block y to block z */

  IF ON (x, y)    CLEAR (x)    CLEAR (z)  ADD LIST ON (x, z)    CLEAR (y)  DELETE

LIST

On (x, y)    CLEAR (z)

A simple breadth-first search will mean that every operator will have to be tried and instantiated in every possible way, as long as the operators' pre-requisites are satisfied. This approach will lead to an exponential tree growth in the search space.

Search strategy – not breadth first but backward chaining

CONDITIONAL PLANNINGConditional planning, also known as contingency planning, deals with incomplete information by constructing a conditional plan that accounts for each possible situation or contingency that may arise.

Conditional planning differs from the ideal planning and execution discussed in the beginning of this lecture. We dealt with cases where we were dealing worlds that are accessible, static and deterministic and the action descriptions must be correct and complete describing all the consequences exactly.

Contingencies are dealt on a one by one basis and the planning program finds out which part of the plan to execute by including sensing actions in the plan to test for the appropriate conditions.

STAGES OF THE PLANNING PROCESS

Stage6: The final stage of planning now involves the addition of the operators Remove(Tyre1) and PutOn(Tyre1). The PutOn operator has the consequent (literal) On and this can be unified by its counterpart in the second of the two Finish states. Our plan to fix the Tyre is now complete.

Intact (Tyre1)

Start

Inflate(Tyre1)

On(Tyre1)Flat (Tyre1)Inflated (Spare)

On(Tyre1)Inflated (Tyre1)

Flat (Tyre1)Intact ((Tyre1)Check(Tyre1)

(Intact (Tyre1))

Finish(Intact (Tyre1))

Finish( ¬ Intact (Tyre1))

On(Spare)Inflated (Spare)

Remove(Tyre1) PutOn(Tyre1)( ¬ Intact (Tyre1))( ¬ Intact (Tyre1))

¬ Intact (Tyre1)