1.1 The Problem This paper addresses the question \why and how is it that we say the same thing di erently to di erent people, or even to the same person in di erent circumstances? " We vary the

For distributed sensor network applications, a practical approach to generating complete interpretations from distributed data must coordinate how separate, concurrently-running systems form, exchange, and fuse their individual hypotheses to form consistent interpretations. Partial global planning provides a framework for coordinating multiple AI systems that are cooperating in a distributed sensor network. By combining a variety of coordination techniques into a single, unifying framework, partial global planning enables separate AI systems to reason about their roles and responsibilities as part of group problem solving, and to modify their planned processing and communication actions to act as a more coherent team. Partial global planning is uniquely suited for coordinating systems that are working in continuous, dynamic, and unpredictable domains because it interleaves coordination with action and allows systems to make effective decisions despite incomplete and possibly obsolete i...

This paper examines the issues that arise in the control of blackboard systems for applications with large and complicated search spaces by analyzing the evolution of blackboard control architectures. We feel that the issues addressed here apply more generally to AI application domains involving complex multi-dimensional search, in which control knowledge is as important to successful problem solving as domain knowledge. Evolution is viewed largely from the context of the Hearsay-II (HSII) speech understanding system. The appeal of the blackboard model is that it provides great flexibility in structuring problem solving. On the other hand, many of the features that are responsible for this flexibility make effective control difficult because they complicate the process of estimating the expected value of potential actions. Among the key themes in the evolution of blackboard control is the development of mechanisms that support more sophisticated goal-directed reasoning. In the basic co...

Sensor interpretation involves the determination of high-level explanations of sensor data. Blackboardbased interpretation systems have usually been limited to incremental hypothesize and test strategies for resolving uncertainty. We have developed a new interpretation framework that supports the use of more sophisticated strategies like differential diagnosis. The RESUN framework has two key components: an evidential representation that includes explicit, symbolic encodings of the sources of uncertainty (SOUs) in the evidence for hypotheses and a script-based, incremental control planner. Interpretation is viewed as an incremental process of gathering evidence to resolve particular sources of uncertainty. Control plans invoke actions that examine the symbolic SOUs associated with hypotheses and use the resulting information to post goals to resolve uncertainty. These goals direct the system to expand methods appropriate for resolving the current sources of uncertainty in the hypothese...

As part of research on sophisticated control for sensor interpretation, we have developed a planningbased control scheme for blackboard systems. A planner's goal/plan/subgoal structure provides explicit context information that can be used to index and apply large amounts of context-specific control knowledge. The key obstacle to using planning for the control of problem solvers is the need to deal with uncertain and dynamically changing situations without incurring unacceptable overhead. We have addressed these problems in several ways: our planner is script-based, planning and execution are interleaved, plans can invoke information gathering actions, plan refinement is controlled by plan-specific focusing heuristics, and the system's focus-of-attention can be dynamically shifted by the refocusing mechanism. Refocusing makes it possible to postpone focusing decisions and maintain the opportunistic control capabilities of conventional blackboard systems. Planning with refocusing result...

The success of large software engineering projects depends critically on the specification, which must represent the requirements of a large number of people with widely differing perspectives. Conventional approaches to software engineering do not address the process of identifying and integrating these perspectives, but instead concentrate on the maintenance of a single consistent description. This results in a specification which represents only one point of view, often the analyst's, excluding suggestions which do not fit with this view. The processes which led to the adoption of this point of view will go unrecorded, making any rationale attached to such a specification incomplete. Other participants will not be able to validate it properly, as it does not relate to their requirements. This thesis integrates ideas drawn from the study of knowledge acquisition, computer-supported co-operative work and negotiation into a model of the specification activity which allows the capture ...

Traditional approaches to task planning assume that the planner has access to all of the world information needed to develop a complete, correct plan which can then be executed in its entirety by an agent. Since this assumption does not typically hold in realistic domains, we have implemented a planner which can plan to perform sensor operations to allow an agent to gather the information necessary to complete planning and achieve its goals in the face of missing or uncertain environmental information. Naturally this approach requires some execution to be interleaved with the planning process. In this paper we present the results of a systematic experimental study of this planner's performance under various conditions. The chief difficulty arises when the agent performs actions which interfere with or, in the worst case, preclude the possibility of the achievement of its later goals. We have found that by making intelligent decisions about goal ordering, what to sense, and when to sens...

Traditional approaches to task planning assume the planner has access to all of the world information needed to develop a complete, correct plan--a plan which can then be executed in its entirety by a robot. We consider prob- lems where some crucial information is missing at plan time but can be obtained from sensors during execution. We discuss the solution of these problems through deferred planning (i.e., by deferring specific planning steps until more complete information is available and then restarting the planner). We also present early results of a comparative study of strategies for deciding which plan steps to defer.

Real world large scale applications require planning systems that combine different planning techniques (like planning ahead, deferred planning and reactive planning). In this paper we present a system (called MRG) that provides this capability. In MRG plans, the planning activities of the planner itself (like plan generation and execution, information acquisition from the real world and reaction to external changes) are explicitly represented. Thus, a MRG plan can describe whether, when and how to perform these activities. As a consequence of this ability (that we call introspective planning), MRG can be used uniformly and flexibly to combine different planning techniques in one system according to the application requirements. 1 Motivations A variety of approaches to planning have been proposed so far in the AI literature, for instance classical planning [3, 18], case-based planning [8], conditional planning [11], deferred planning [2], reactive systems [1, 5]. Each of the...

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 . . . .