Planning-Based Integrated Decision Support Systems

This paper describes a system that uses AI planning and representation techniques as the core of a decision support system. * The planning technology is supplemented with other AI and non-AI technologies. The overall system and initial application domain, military operations planning, are described first. We then describe the integration of SIPE-2, a generative planning system, with three independently-developed AI systems: a temporal reasoning engine, a case-based force selection module, a system for scheduling and capacity analysis. 1 INTRODUCTION In tackling a real-world problem with an AI solution, it is not uncommon to find that a single AI system fails to meet all the problem requirements. In some cases, existing software can be integrated (or must be used) with the AI solution; or the AI system must be altered to fit the new problem (e.g., with a customized user interface); or new software must be added to round out the capabilities of the single system. In this paper, we rep..

SOCAP: System for Operations Crisis Action Planning

In this paper, we report on our past and recent experiences in applying an AI generative planning system, called System for Interactive Planning and Execution (SIPE-2), to the problem of generating crisis action operations plans in a joint military domain * . We describe our motivation for selecting a generative planner, the application itself (including the second Integrated Feasibility Demonstration, IFD-2) , and the lessons we learned in creating it. We also report on the applied research we performed to address the lessons learned in IFD-2. This involved integrating the generative planner with several complementary technologies that were available through the Planning Initiative (PI): a temporal reasoner, a case-based reasoner, and the capacity analysis component of a scheduling system. These technology integration experiments (TIEs) were executed within the Common Prototyping Environment (CPE). We discuss both the unique characteristics of each TIE and the general features that ..

Effects of explanation support on learning genetics

This study examined the effects that different kinds of technology-based representational tools have on students’ genetics learning. One form of tool represented phenomenological features of genetics – genes, pedigrees, and so on – and were embedded in a simulations-based software program. and another tool provided discursive representation to support students ’ construction of explanations. In a quasi-experimental study, students completed a 3-week unit on genetics. Students in one condition learned genetics using only the phenomenological tool and exploring simulations of population level genetic phenomena; students in a second condition used this same tool for the same activities and used a word processor to write explanations of events that occurred in the simulations; and students in a third condition used the phenomenological tool to complete the same activities, and wrote explanations of the simulations using the discursive tool. Pre- and post-assessments of students ’ understanding of basic genetics concepts showed no differences among conditions. A post-test explanation task, however, showed that students who had learned genetics with explicit, discursive support for explanation construction were significantly more likely to correctly apply genetics concepts to explain a specific problem than students who learned genetics without explanation construction supports. We argue that students ’ efforts to explain provided a context for organizing genetics ideas into a coherent conceptual framework, and that the discursive representations provided key features o