Identifying facts for TCBR

Paper presented at The Sixth International Conference on Case-Based Reasoning, Chicago, IL.This paper explores a method to algorithmically distinguish case-specific facts from potentially reusable or adaptable elements of cases in a textual case-based reasoning (TCBR) system. In the legal domain, documents often contain casespecific facts mixed with case-neutral details of law, precedent, conclusions the attorneys reach by applying their interpretation of the law to the case facts, and other aspects of argumentation that attorneys could potentially apply to similar situations. The automated distinction of these two categories, namely facts and other elements, has the potential to improve quality of automated textual case acquisition. The goal is ultimately to distinguish case problem from solution. To separate fact from other elements, we use an information gain (IG) algorithm to identify words that serve as efficient markers of one or the other. We demonstrate that this technique can successfully distinguish case-specific fact paragraphs from others, and propose future work to overcome some of the limitations of this pilot project

KLEOR: A Knowledge Lite Approach to Explanation Oriented Retrieval

In this paper, we describe precedent-based explanations for case-based classification systems. Previous work has shown that explanation cases that are more marginal than the query case, in the sense of lying between the query case and the decision boundary, are more convincing explanations. We show how to retrieve such explanation cases in a way that requires lower knowledge engineering overheads than previously. We evaluate our approaches empirically, finding that the explanations that our systems retrieve are often more convincing than those found by the previous approach. The paper ends with a thorough discussion of a range of factors that affect precedent-based explanations, many of which warrant further research

Systematically evolving configuration parameters for computational intelligence methods

Paper presented at The First International Conference (PReMI 2005); LNCS 3776: pp. 376-381.The configuration of a computational intelligence (CI) method is responsible for its intelligence (e.g. tolerance, flexibility) as well as its accuracy. In this paper, we investigate how to automatically improve the performance of a CI method by finding alternate configuration parameter values that produce more accurate results. We explore this by using a genetic algorithm (GA) to find suitable configurations for the CI methods in an integrated CI system, given several different input data sets. This paper describes the implementation and validation of our approach in the domain of software testing, but ultimately we believe it can be applied in many situations where a CI method must produce accurate results for a wide variety of problems

A Real-time Strategy Agent Framework and Strategy Classifier for Computer Generated Forces

This research effort is concerned with the advancement of computer generated forces AI for Department of Defense (DoD) military training and education. The vision of this work is agents capable of perceiving and intelligently responding to opponent strategies in real-time. Our research goal is to lay the foundations for such an agent. Six research objectives are defined: 1) Formulate a strategy definition schema effective in defining a range of RTS strategies. 2) Create eight strategy definitions via the schema. 3) Design a real-time agent framework that plays the game according to the given strategy definition. 4) Generate an RTS data set. 5) Create an accurate and fast executing strategy classifier. 6) Find the best counterstrategies for each strategy definition. The agent framework is used to play the eight strategies against each other and generate a data set of game observations. To classify the data, we first perform feature reduction using principal component analysis or linear discriminant analysis. Two classifier techniques are employed, k-means clustering with k-nearest neighbor and support vector machine. The resulting classifier is 94.1% accurate with an average classification execution speed of 7.14 us. Our research effort has successfully laid the foundations for a dynamic strategy agent

Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances

Computação ubíqua para aplicações em saúde

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200

TAARAC : test d'anglais adaptatif par raisonnement à base de cas

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal