Frequently we become amazed with the increasing number of problems to be solved that fiourish while facing daily activities. Often, related to these problems we llave also an incredible amount oí data. Since we cannot allways afford time and resources…

Current reasoning systems attempt to model an agent's knowledge and interaction with its environment in a symbolic manner. This environment, its world is generally dynamic and changing due to natural evolution or the actions of other agents that are a…

Classical methods for representing and reasoning with knowledge rely on the assumption that the available information is complete, certain and consistent. In real-world problems this is usually not the case, and Al has long dealt with the issue of finding…

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML…

The notion of rational agency was proposed by Russell [9] as an alternative characterization of intelligence agency. Loosely speaking, an agent is said to be rational if it perfomns the right actions according to the information it possesses and the…

The purpose of this paper is to describe how some theorems about constructions in categories can be seen as a way of doing generic programming. No prior knowledge of category theory is required to understand the paper.
We explore the…

To find a good termination criterion for genetic algorithms is a difficult and frequently ignored task. In most instances the practitioner stops the algorittm after a predefined number of generations or function evaluations. How this number is established? This stop…