this paper. In particular, the protocols we have collected often involve picturing different situations (e.g., a mental map of South America, images of savannas, or an advertisement showing Juan Valdez on his coffee plantation in Colombia). These im- ages can be taken as evidence for the manipulation of mental models in Johnson-Laird's terms. But overlaying this manipulation of mental models are the systematic patterns in which they are deployed to support one's con- clusions (cf. Rips, 1986). So while mental models may be part of the story of plausible reasoning, there is another critical part which the theory we pro- pose addresses. The theory does not address the issue of whether people make systematic errors in their reasoning, as the psychological literature on decision making (Kahneman, Slovic, & Tversky, 1982) attempts to document. This issue does not arise in the theory because we are developing a formalism for representing the kinds of inferences people make and the parameters that affect their certainty, rather than a theory about how people make particular inferences. People may systematically ignore some kinds of information or undervalue particular certainty parameters--we have not attempted to determine whether they do or not. Instead we have tried to represent all the kinds of reasoning patterns and the kinds of certainty parameters that appear in the protocols we have analyzed (Collins, 1978a, 1978b). In this regard it is worth pointing out that certain fallacles in logic, such as affirming the consequent (Havi- land, 1974), become plausible inference patterns in the theory.' The theory was developed to account for protocols where. a question drives the search fo relevant information; in Artificial Intelligence this is called backward inferencing. One qu...

This paper presents a view of planning as a task supported by a dynamic memory. This view attempts to integrate models of memory, learning and planning into a single system that learns about planning by creating new plans and analyzing how they interact with the world. We call this view of planning Case-Based Planning. A case-based planner makes use of its own past experience in developing new plans. It relies on its memory of observed effects, rather than a set of causal rules, to create and modify new plans. Memories of past successes are accessed and modified to create new plans. Memories of past failures are used to warn the planner of impending problems, and memories of past repairs are called upon to tell the planner how to how to deal with them. This view of planning from experience supports and is supported by a learning system that incorporates new experiences into the planner's episodic memory. This learning algorithm gains from the planner's failures as well as its successe...

This chapter provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research.

In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires declarative representations of meta-knowledge of the reasoning performed by the system during the performance task, of the system's knowledge, and of the organization of this knowledge. This paper presents a taxonomy of possible reasoning failures that can occur during a performance task, declarative representations of these failures, and associations between failures and particular learning strategies. The theory is based on Meta-XPs, which are explanation structures that help the system identify failure types, formulate learning goals, and choose appropriate learning strategies in order to avoid similar mistakes in the future. The theory is implemented in a ...

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent tutoring system with tutorial dialogue in natural language. Observational analyses revealed significant relationships between learning and the affective states of boredom, flow and confusion. The positive correlation between confusion and learning is consistent with a model that assumes that cognitive disequilibrium is one precursor to deep learning. The findings that learning correlates negatively with boredom and positively with flow are consistent with predictions from Csikszentmihalyi’s analysis of flow experiences.

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as online sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (Self-Improving Navigation System). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addr...

Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate exp...

This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner’s affective state, presuming that this will promote learning. We integrate state-of-the-art, nonintrusive, affect-sensing technology with AutoTutor in an endeavor to classify emotions on the bases of facial expressions, gross body movements, and conversational cues. This paper sketches our broad theoretical approach, our methods for data collection and evaluation, and our emotion classification techniques.

Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in...