In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

We consider a new form of co-evolutionary learning in which human students and software tutors become partners in a cooperative learning process. While the students are learning from the tutors, the tutors will also be learning how to do their job more effectively through their interactions with the students. Earlier work on game-playing machine learners has brought to light important issues for co-evolutionary learning; in particular, that these interactions can be modeled as a meta-game between teachers and students, and that learning may fail due to suboptimal equilibria -- for example, because of collusion between the individual players -- in this meta-game of learning. We discuss some of the challenges that these issues may raise for the designers of intelligent software tutoring systems and describe a prototype Java applet that we have built in an effort to explore how these challenges may best be met. 1. Introduction Advancing technology is opening up exciting new possibilitie...

. It is widely reported in the literature that incremental clustering systems suffer from instance ordering effects and that under some orderings, extremely poor clusterings may be obtained. In this paper we present a new general strategy aimed to mitigate these effects, the Not-Yet strategy which has a general and open formulation and it is not coupled to any particular system. Unlike other proposals, this strategy maintains the incremental nature of learning process. In addition, we propose a classification of strategies to avoid ordering effects which clarifies the benefits and disadvantages we can expect from the proposal made in the paper as well from existing ones. A particular implementation of the Not-Yet strategy is used to conduct several experiments. Results suggest that the strategy improves the clustering quality. We also show that, when combined with other local strategies, the Not-Yet strategy allows the clustering system to get high quality clusterings. Keywords: Machin...

. It is widely reported in the literature that incremental clustering systems suffer from instance ordering effects and that under some orderings, extremely poor clusterings may be obtained. In this paper we present a new strategy aimed to mitigate these effects, the Not-Yet strategy which has a general and open formulation and it is not coupled to any particular system. Results suggest that the strategy improves the clustering quality and also that performance is limited by its limited foresight. We also show that, when combined with other strategies, the Not-Yet strategy may help the system to get high quality clusterings. 1 Introduction Ideally, intelligent agents should possess the ability of adapting their behavior to the environment over time through learning. Thus, learning methods should be able of updating a knowledge base in a continual basis as new experience is gained. Particularly, if an agent performing a clustering task [5] should be able of using its learned knowledge ...

In this work we analyze the most relevant, in our opinion, algorithms for learning Bayesian Networks. We analyze methods that use goodness-of-fit tests between tentative networks and data. Within this sort of learning algorithms we distinguish batch and incremental methods. Finally, we propose a system, called BANDOLER, that incrementally learns Bayesian Networks from data and prior knowledge. The incremental fashion of the system allows to modify the learning strategy and to introduce new prior knowledge during the learning process in the light of the already learnt structure. 1 Introduction The aim of this work is twofold. On the one hand, we introduce the state of the art on learning Bayesian networks. It is intended to be a tutorial on the learning methods based on goodness-of-fit tests. We present the most significant, in our opinion, learning algorithms found in the literature, as well as the theory they are based on. On the other hand, we propose a research framework. The fiel...

Agents that exist in an environment that changes over time, and are able to take into account the temporal nature of experience, are commonly called incremental learners. It is widely known that incremental learning systems suffer from order effects, a phenomenon observed when differently ordered sequences of examples lead to different results. The goal of this paper is presenting INTHELEXback , an order-independent evolution of the incremental learning system INTHELEX. A backtracking strategy is incorporated in its refinement operators, which causes a change in its refinement strategy and reflects the human behavior during the learning process. It consists in remembering the different versions of the learned theory across modifications due to new evidence. In this way the system can backtrack on a previous knowledge level when it discovers to have made a wrong choice. Experiments on an artificial dataset validate the approach in terms of computational cost and predictive accuracy.

This paper addresses the problem of mitigating the order e#ects in incremental learning, a phenomenon observed when di#erent ordered sequences of observations lead to di#erent results. A modification of an ILP incremental learning system, with the aim of making it order-independent, is presented. A backtracking strategy on theories is incorporated in its refinement operators, which causes a change of its refinement strategy and reflects the human behavior during the learning process. A modality to restore a previous theory, in order to backtrack on a previous knowledge level, is presented. Experiments validate the approach in terms of computational cost and predictive accuracy.

Ingcreasingly, data-mining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures to store sufficient statistics in main memory and to accelerate the mining process in batch environments. Here we present an efficient method to sequentially update ADtrees that is suitable for incremental environments. 1.

Abstract- While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends to make domainspecific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously involved a genetic-programming based CFG inference system, we discuss improvements made to an incremental learning algorithm, called GenInc, for inferring context-free grammars with a core focus on facilitating domain-specific language development. We elaborate on the enhancements made to GenInc in the form of new operators, and conclude by discussing the results of applying GenInc to domain-specific languages.

It is widely reported in the literature that incremental clustering systems suffer from instance ordering effects and that under some orderings extremely poor clusterings may be obtained. In this paper we present a new general strategy aimed to mitigate these efects, the Not-Yet strategy, which has a general and open formulation and it is not coupled to any particular system. In addition, we propose a classification of strategies to avoid ordering effects which clarifies the benefits and disadvantages we can expect from the proposal made in the paper as well from existing ones. A particular implementation of the Not-Yet strategy is used to conduct several experiments. Results suggest that the strategy can improve the clustering quality and also that performance is limited by its local nature. We also show that, when combined with other local strategies, the Not-Yet strategy may help the system to get high quality clusterings. The observed benefits and limitations suggest future work un...