Machine learning offers the possibility of designing intelligent systems that refine and improve their initial knowledge through their own experience. This article focuses on the problem of learning sequential decision rules for multi-agent environments. We describe the SAMUEL learning system that uses genetic algorithms and other competition based techniques to learn decision strategies for au...

Peer-to-Peer (P2P) computing (also called ‘public-resource computing’) is an effective approach to perform computation of large tasks. Currently used P2P computing systems (e.g., BOINC) are most often centrally managed, i.e., the final result of computations is created at a central node using partial results – what may be not efficient in the case when numerous participants are willing to downl...

In an incremental NLP pipeline every module needs to work incrementally. However, an incremental processing mode can lead to a degradation of accuracy due to the missing context to the right. We discuss three properties of incremental output that can be traded for accuracy, namely timeliness, monotonicity and decisiveness. The consequences of these trade-offs are evaluated systematically for th...

This article describes the application of computational decision analytic techniques for a national policy decision. It constitutes an example of the increasing use of modern computational decision methods to assist in decision-making in society. An integrated flood catastrophe model is presented as well as some results of a case study made in the Upper Tisza region in north-eastern Hungary. Ba...

In this paper, a decision theoretic view of the goal of surveillance is adopted. Subsequently a very simple formal model of the environment is introduced with the purpose of deening and comparing surveillance strategies. Several existing surveillance strategies are discussed. Then our decision-theoretic strategy is proposed and compared with the existing strategies on a theoretical level. In or...

An optimal strategy in a Markov decision problem is robust if it is optimal in every decision problem (not necessarily stationary) that is close to the original problem. We prove that when the state and action spaces are finite, an optimal strategy is robust if and only if it is the unique optimal strategy.

An adaptive control system for computational intelligence agent within a data mining multi-agent system is presented. As opposed to other approaches concerning a fixed control mechanism, the presented approach is based on evolutionary trained decission trees. This leads to control approach created adaptively based on data tasks the agent encounters during its adaptive phase. A pilot implementat...

The tradeoff between pursuing a known reward (exploitation) and sampling unknown, potentially better opportunities (exploration) is a fundamental challenge faced by all adaptive organisms. Theories formalize the value of exploration (gathering information) as an information bonus. However, this may be difficult to compute; a simpler alternative is to increase decision noise, driving random expl...

A Boolean value of given a priori probability distribution is transmitted to a deciding agent by several processes. Each process fails independently with given probability, and faulty processes behave in a Byzantine way. A deciding agent has to make a decision concerning the transmitted value on the basis of messages obtained by processes. We construct a deterministic decision strategy which ha...

This work describes a method to control a behaviour of intelligent data mining agent. We developed an adaptive decision making system that utilizes genetic programming technique to evolve an agent’s decision strategy. The parameters of data mining task and current state of an agent are taken into account by tree structures evolved by genetic programming. Efficiency of decision strategies is com...