Chapter 3: Reasoning Using Cases

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

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

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

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

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

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

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

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

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

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

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

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