Model-Based and Case-Based Reasoning in Artificial Intelligence, Lecture notes of Artificial Intelligence

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Strong Method Problem Solving

(Topic 7-part b)

Course Contents

Again..Selected topics for our course. Covering all of AI is

impossible!

Key topics include:

Introduction to Artificial Intelligence (AI)

Knowledge Representation and Search

Introduction to AI Programming

Problem Solving Using Search

Exhaustive Search Algorithm

Heuristic Search

Techniques and Mechanisms of Search Algorithm

Knowledge Representation Issues and Concepts

Strong Method Problem Solving

Soft Computing and Machine Learning

Model-based reasoning

 Often expert systems applied heuristics in

inappropriate situations

 Limitation model-based tries to address

 What is model-based system?

 A knowledge-based reasoner whose analysis

is founded directly on the specifications and

functionality of a physical system

 Creates a software simulation

 i.e create model for electronic circuit

What is model-based (MB) reasoning

system?

 i.e. in trouble shooting faults in physical

system,

 Model leads to predicted behaviors

 Fault ~ discrepancies between predicted and

expected behavior

 MB tells user what to expect, when

observations differ from expectations,

discrepancies leads to identification of faults

• circuit of three multipliers and two adders
• task is to determine where the fault lies that will explain discrepancies

Model-Based reasoner: Example 2

Taking advantage of direction of information flow, after

Davis and Hamscher (1988).

• idealized schematic of main engine subassembly-complex spacecraft
• its function to isolate failed components permanently

Model-Based reasoner: Example 3

A schematic of the simplified Livingstone propulsion

system, from Williams and Nayak (1996)-NASA.

 Knowledge-intensive technique that supports the

reuse of past experience in a problem domain to

address new situations

 Another powerful strategy experts use –

reasoning from case, examples of past problems

and solution

 CBR uses explicit database of problem solutions

to address new problem solving situations

What is case-based (CBR)

reasoning system?

 Solutions may be collected from:

 Human experts

 Results of previous search-based success or

failures

 Medical education – depends heavily on case

histories and experience with other patients

 Lawyers- select past law cases to convince

court

 Programmers- reuse code

What is case-based (CBR)

reasoning system?

CBR ….

Kolodner (1993) offers a set of possible preference heuristics to help organize the

storage and retrieval of cases. These include:

1. Goal-directed preference. Organize cases, at least in part, by

goal descriptions. Retrieve cases that have the same goal as the

current situation.

2. Salient-feature preference. Prefer cases that match the most

important features or those matching the largest number of

important features.

3. Specify preference. Look for as exact as possible matches of

features before considering more general matches.

4. Frequency preference. Check first the most frequently matched

cases.

5. Recency preference. Prefer cases used most recently.

6. Ease of adaptation preference. Use first cases most easily

adapted to the current situation.

CBR….Transformational analogy, adapted from Carbonell

• learning through analogy
• transformational analogy-solve new problem by modifying existing solutions til they may be applied to new instance
• operators modify by inserting, deleting, reordering etc.

 Advantages of model-based reasoning:

1. Ability to use functional/structural knowledge of the domain.

Increase reasoner’s ability to handle a variety of problems,

including those that may not have been anticipated by system’s

designers

2. Model-based reasoners tend to be very robust. For the same

reason that humans often retreat to first principles when

confronted with a novel problem, model-based reasoners tend to

be thorough and flexible problem solvers

3. Some knowledge is transferable between tasks. Model-based

reasoners are often built scientific, theoretical knowledge.

Because science strives for generally applicable theories, this

generality often extends to model-based reasoners

4. Often, model-based reasoners can provide causal explanations.

These can convey a deeper understanding of the fault to human

users, and can also play an important tutorial role.