Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

Progressive Horizon Planning

In an earlier paper [Rymon et a1 89], we showed how domain localities and regularities can be used to reduce the complexity of finding a trauma management plan that satisfies a set of diagnostic and therapeutic goals. Here, we present another planning idea - Progressive Horizon - useful for optimizing such plans in domains where planning can be regarded as an incremental process, continuously interleaved with situation - goals analysis and plan execution. In such domains, planned action cannot be delayed until all essential information is available: A plan must include actions intended to gather information as well as ones intended to change the state of the world. Interleaving planning with reasoning and execution, a progressive horizon planner constructs a plan that answers all currently known needs but has only its first few actions optimized (those within its planning horizon). As the executor cames out actions and reports back to the system, the current goals and the plan are updated based on actual performance and newly discovered goals and information. The new plan is then optimized within a newly set horizon. In this paper, we describe those features of a domain that are salient for the use of a progressive horizon planning paradigm. Since we believe that the paradigm may be useful in other domains, we abstract from the exact techniques used by our program to discuss the merits of the general approach

Progressive Horizon Planning - Planning Exploratory-Corrective Behavior

Much planning research assumes that the goals for which one plans are known in advance. That is not true of trauma management, which involves both a search for relevant goals and reasoning about how to achieve them. TraumAID is a consultation system for the diagnosis and treatment of multiple trauma. It has been under development jointly at the University of Pennsylvania and the Medical College of Pennsylvania for the past eight years. TraumAID integrates diagnostic reasoning, planning and action. Its reasoner identifies diagnostic and therapeutic goals appropriate to the physician’s knowledge of the patient’s state, while its planner advises on beneficial actions to next perform. The physician’s lack of complete knowledge of the situation and the time limitations of emergency medicine constrain the ability of any planner to identify what would be the best thing to do. Nevertheless, TraumAID’s Progressive Horizon Planner has been designed to create a plan for patient care that is in keeping with the standards of managing trauma