chapter 3: reasoning using cases

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 1

Chapter 3: Reasoning Using Cases

• In this chapter, we look at how cases are used to reason

• We’ve already seen that there are two main types of CBR

• Problem solving: planning (CHEF), design (JULIA), diagnosis (CASEY)

• Interpretation: understanding, justification and projection (HYPO)

• Both types of CBR may be used in the same system

• MEDIATOR was an early CBR system that solved disputes

• It was a problem solving system in that it generated plans to end disputes

• It was interpretive in that it had to understand the reasons behind a dispute in order to propose a good compromise


Problem Solving and Interpretive Tasks Addressed by CBR: Planning

• Planning is the process of generating a sequence of steps for achieving some desired state

• Planning is a difficult task

• The order of steps is important

• You need to project the consequences of executing each step

• You need to be sure the preconditions for a step to succeed are met

• Checking for interactions with traditional planning techniques is exponential in the number of steps in a plan

• Things don’t always go according to plan

• You may need to replan quickly if things go wrong


Planning, continued

• CBR helps by storing complete plans that already have the step interactions worked out

• Plans are normally stored so that parts of plans, as well as whole plans, can be accessed

• This facilitates quickly changing plans, when needed

• Examples of CBR planning systems include:

• CHEF

• A system used by the Italian Forest Service to plan the management of large forest fires

• A system used by the U.S. Navy to plan the evacuation of civilians who get caught in the middle of dangerous situations


Design

• Design is the process of describing some concrete object that satisfies a set of constraints• Note that nothing in the design tells you how to GET the desired

object. For that, you would need a plan.• In design, problems may be underconstrained or overconstrained

• An underconstrained problem has few constraints and many possible solutions• If JULIA needs to find a menu that’s cheap and easy to prepare,

there are hundreds of possibilities. That’s fine for a human designer, but doesn’t give a system much to go on

• An overconstrained problem has constraints that conflict with each other• There may be no solution at all that meets all of the constraints.

Again, humans may be able to deal with this, but a system can not find a solution in this situation


Design, continued

• The underconstrained case is more common, found, for example, in designing buildings and cars and menus

• Here, CBR helps by providing examples of good solutions

• For example, a car designer considers past models in designing next year’s models

• CBR systems have been used to design menus (JULIA), autoclave oven layouts (CLAVIER), buildings, landscapes, and mechanical devices


Explanation and Diagnosis

• In explanation, we find reasons why something happened

• This is sometimes called the credit assignment or the blame assignment problem

• Diagnosis is the most common type of explanation application

• The input to a diagnostic system is a list of symptoms or problems, and the output is an explanation for these problems

• CASEY diagnoses heart failures, and PROTOS diagnoses hearing disorders

• Troubleshooting is a real world problem in which diagnostic CBR systems are used

• Deployed systems are typically simpler than CASEY or PROTOS

• In a computer support help desk, for example, adaptation and justification can be omitted

• If a close match is not found, a technician is called to assist

• Many common problems will have solutions already stored


Justification and Adversarial Reasoning

• Adversarial reasoning means making convincing arguments that our own position is right and that our opponent’s position is wrong

• Justification is the same thing, except that there need not be an opponent• HYPO is an adversarial reasoning system

• When a new case comes in, HYPO finds relevant features and retrieves similar cases

• Some similar cases will support the lawyer’s position and some will oppose it

• HYPO makes a 3-ply argument• It starts with a supporting case to make an initial argument• It takes an opposing case to make counterarguments• It finds additional supporting cases to counter the

counterarguments• This helps lawyers avoid being surprised by adversaries in court


Classification and Interpretation

• Interpretation, or understanding a current situation, is often a process of determining if the current situation fits a particular type of classification

• If we know what type of problem we have, we’re better able to deal with it

• There is no general purpose methodology for getting a computer to understand a situation

• CBR systems do this is domain dependent ways

• PROTOS and HYPO do this by comparing cases based on features they determine ahead of time are important


Projection

• Projection is predicting the effects of a decision or a plan

• It helps in evaluating proposed solutions

• If we can predict that broccoli will get soggy in advance, we can avoid making it soggy to begin with

• Projection is especially important in planning, to help ensure that the steps we take lead toward our goal

• Battle Planner is a CBR system used for projection

• This was used at West Point, a military academy, to train cadets

• Student commanders plan battle strategies, and Battle Planner tells them if they would win or lose

• Battle Planner’s cases are real historical battles

• If it projects a loss, the student commander knows to change the proposed strategy and learns to be a better military tactician


Case-Based Reasoning vs. Rule-Based Reasoning

• Rule-based reasoning (RBR) is the traditional way in which expert systems were built

• A rule is a knowledge representation expressing a relationship among objects. It contains a piece of knowledge that can be combined, or chained together, with other pieces of knowledge to build a solution to a problem

• The major differences between CBR and RBR are:• Rules are patterns. Cases are constants.• Rules are fired that match input exactly. Cases are retrieved that

match input partially.• Rules are applied in an iterative cycle of small events. Cases are

retrieved that approximate an entire solution and are then adapted.• Rules are small, ideally independent but consistent, pieces of

domain knowledge. Cases are large chunks of domain knowledge, possibly redundant, in part, with other cases.


Case-Based Reasoning vs. Model-Based Reasoning

• In Model-Based Reasoning (MBR), inferences are made based on some physical or mathematical model of the problem domain.

• CASEY relied on an MBR system that used a physiological model of the human heart

• Major differences between CBR and MBR are:

• MBR systems store causal models of devices or domains. CBR systems store examples of devices or satisfactory solutions for a domain.

• MBR requires that a formal model exists. CBR can work whether a domain is formalizable or not.

• MBR is good for evaluating proposed solutions, but doesn’t tell how to generate a solution to begin with. CBR gives example solutions that can be adapted or reused.


Multi-Modal Reasoning

• Multi-Modal Reasoning (MMR) systems solve problems by combining multiple reasoning modalities or approaches.

• These include, but are not limited to, RBR and MBR

• This quarter, we are studying CBR

• In building real systems, it is often advantageous to combine CBR with other approaches

• Don’t let anyone (even me!) tell you that there is a single AI approach that is right for all problems

chapter 3: reasoning using cases

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