Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case- based reasoning, describes some of the leading methodo- logical approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summa-rized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

A novel task instruction method for future intelligent robots is presented. In our method, a robot learns reusable task plans by watching a human perform assembly tasks. Functional units and working algorithms for visual recognition and analysis of human action sequences are presented. The overall system is model based and integrated at the symbolic level. Temporal segmentation of a continuous task performance into meaningful units and identification of each operation is processed in real time by concurrent recognition processes under active attention control. Dependency among assembly operations in the recognized action sequence is analyzed, which results in a hierarchical task plan describing the higher level structure of the task. In another workspace with a different initial state, the system re-instantiates and executes the task plan to accomplish an equivalent goal. The effectiveness of our method is supported by experimental results with block assembly tasks. Keywords--- Learni...

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One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case’s adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional, similarity assumption in an effort to deliver more sophisticated and scaleable reasoning systems. Smyth & Keane 3 Adaptation-Guided Retrieval 1

We describe how our planner, xfrm, carries out the process of anticipating and forestalling execution failures. xfrm is a planning system that is embedded in a simulated robot performing a varying set of complex tasks in a changing and partially unknown environment. xfrm revises plans controlling the robot while they are executed. Thus whenever the robot detects a contingency, xfrm projects the effects of the contingency on its plan and---if necessary---revises its plan in order to make it more robust. Using xfrm, the robot can perform its tasks almost as efficiently as it could using efficient default plans, but much more robustly. Revising default plans requires xfrm to reason about full-fledged robot plans and diagnose various kinds of plan failures that might be caused by imperfect sensing and effecting, incomplete and faulty world models, and exogenous events. To this end, xfrm reasons about the structure, function, and behavior of plans, and diagnoses projected plan failures by...

CONTENTS Page ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . iii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . vi CHAPTER I. INTRODUCTION . . . . . . . . . . . . . . . . 1 1.1 An Overview . . . . . . . . . . . . . . . 1 1.2 Evolution of Computer-Based Instruction at Rush . . . . . . . . . . . . . . . . . 3 1.3 Goals of the Thesis . . . . . . . . . . . 4 1.4 Organization of the Thesis . . . . . . . . 6 II. THE BACKGROUND . . . . . . . . . . . . . . . 9 2.1 Qualitative Reasoning . . . . . . . . . . 9 2.2 Subject Area . . . . . . . . . . . . . . 10 2.3 Organization . . . . . . . . . . . . . . 12 2.4 System Constraints . . . . . . . . . . . 14 2.5 Multiple Simultaneous Inputs . . . . . . . 15 III. ORGANIZATION OF CIRCSIM-TUTOR . . . . . . . . 18 3.1 Intelligent Tutoring Systems . . . . . . . 18 3.2 Domain Expertise . . . . . . . . . . . . 23 3.3 Input-Understander . . . . . . . . . . . 26 3.4 Student Modeler . . . . . . . . . . . . . 27 3.5 Instructional Planner . . . .

. We describe xfrml, the transformation language of the planning system xfrm. xfrm is embedded in a simulated robot that performs jobs in a changing and partly unknown environment. xfrml allows xfrm to anticipate and forestall many common flaws in autonomous robot behavior that cannot be dealt with by other planning representations. In order to diagnose execution failures in projected execution scenarios, xfrm has to infer whether or not particular parts of the plan were projected to be executed and why. The use of xfrml makes such inferences possible because xfrml not only represents the physical effects of plan execution, but also the process of plan interpretation, as well as temporal, causal, and teleological relationships among plan interpretation, the world, and the physical behavior of the robot. 1 Introduction Autonomous robots acting in changing and partly unknown environments cannot commit in advance to a fixed course of action. Rather, they have to be flexible and make cr...

Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm's intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DSS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making. D 2002 Elsevier Science B.V. All rights reserved.

... behavior of planetary rovers more robust for the purpose of increased productivity. Due to the inherent uncertainty in rover exploration, the traditional approach to rover control is conservative, limiting the autonomous operation of the rover and sacrificing performance for safety. Our objective is to increase the science productivity possible within a single uplink by allowing the rover's behavior to be specified with flexible, contingent plans and by employing dynamic plan adaptation during execution. We have deployed a system exhibiting flexible, contingent execution; this paper concentrates on our ongoing efforts on plan adaptation. Plans can

Abstract. An important step in the solution of a target problem in case-based reasoning (CBR) is the retrieval of similar previous cases that can be used to solve the target problem. We review a selection of papers from the CBR literature on aspects of retrieval, such as approaches to the assessment of surface and structural similarity and techniques for automating the construction and maintenance of similarity measures. We also examine a number of retrieval techniques that have been developed to address the limitations of retrieval based purely on similarity. 1