CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling1 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 Marling2 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 Marling3 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 Marling4 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 Marling5 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 Marling6 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 Marling7 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 Marling8 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 Marling9 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 Marling10 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 Marling11 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 Marling12 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