Abstract
In recent years various methods from the field of artificial intelligence (AI) have been applied to economic problems. The subarea of multiagent systems (MAS) is particularly useful as it enables to simulate individuals or organizations and various interactions among them. In this paper we investigate a scenario with a set of agents, each belonging to a certain sector of activity (e.g. agriculture, clothing, health sector etc.). The agents produce, consume goods or services in their area of activity. Besides, our model includes also the resource of free time. The goods and resources are exchanged on a market governed by auction, which determines the prices of all goods. We discuss the problem of developing an adaptive producer that exploits reward-based learning. This facet enables the agent to exploit previous information gathered and adapt its production to the current conditions. We describe a set of experiments that show how such information can be gathered and explored in decision making. Besides, we describe a scheme that we plan to adopt in a full-fledged experiments in near future.

Abstract

Abstract
Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We also follow a different path to estimate dataset similarity based on data characteristics. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. The variants of our method that rely on crossvalidation tests to estimate dataset similarity provides better solutions than those that rely on data characteristics.

Abstract
This paper presents an inductive method that learns a logic program represented as an ordered list of clauses. The input consists of a training set of positive examples and background knowledge represented intensionally as a logic program. Our method starts by constructing the explanations of all the positive examples in terms of background knowledge, linking the input to the output arguments. These are used as candidate hypotheses and organized, by relation of generality, into a set of hierarchies (forest). In the second step the candidate hypotheses are analysed with the aim of establishing their effective coverage. In the third step all the inconsistencies are evaluated. This analysis permits to add, at each step, the best hypothesis to the theory. The method was applied to learn the past tense of English verbs. The method presented achieves more accurate results than the previous work by Mooney and Califf [7]. © Springer-Verlag Berlin Heidelberg 1998.

Abstract

Abstract