Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner's decision points and integration in PRODIGY is achieved via mutually interpretable knowledge structures. This article describes the PRODIGY planner, briefly reports on several learning modules developed earlier along the project, and presents in more detail two recently explored methods to learn to generate plans of better quality. We introduce the techniques, illustrate them with comprehensive examples, and show prelimary empirical results. The article also includes a retrospective discussion of the characteristics of the overall PRODIGY architecture and discusses their evolution within the goal of the project of building a large and robust integrated planning and learning system.
We describe a technique for fast compression of time series, indexing of compressed series, and retrieval of series similar to a given pattern. The compression procedure identifies "important" points of a series and discards the other points. We use the important points not only for compression, but also for indexing a database of time series. Experiments show the effectiveness of this technique for indexing of stock prices, weather data and electroencephalograms.
The choice of the right problem-solving method, from available methods, is a crucial skill for experts in many areas. We describe a technique for automatic selection among methods, based on analysis of their past performances. We formalize the statistical problem involved in choosing an e cient method, derive a solution to this problem, and describe a selection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method, if it takes too much time. We then extend the basic statistical technique to account for problem sizes and similarity among problems. We apply this technique to select among search engines in the prodigy system, and then test the selection technique on arti cially generated performance data, with several probability distributions.
The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual procedure.We describe an expert system that helps to select patients for clinical trials. If the available data are insufficient for choosing patients, the system suggests additional medical tests and finds an ordering of the tests that reduces their total cost. Experiments show that the system can increase the number of selected patients. We also present an interface that enables a medical researcher to add clinical trials and selection criteria without the help of a programmer. The addition of a new trial takes ten to twenty minutes, and novice users learn the functionality of the interface in about an hour.
& We present a formal description of the planning algorithm used in the Prodigy4.0 system. The algorithm is based on an interesting combination of backward-chaining planning and simulation of plan execution. The backward-chainer selects goal-relevant operators, and then Prodigy simulates their application to the current state of the world. The system can use different backward-chaining procedures, some of which are presented in the paper.
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