Catálogo de publicaciones - libros

An effective agent-based design approach is significant in engineering agent-based systems. Existing design approaches meet with challenges in designing Internet-based open agent systems. The emergence of service-oriented computing (SOC) brings in intrinsic mechanisms for complementing agent-based computing (ABS). In this paper, we investigate the dialogue between agent and service, and between ABS and SOC. As a consequence, we synthesize them and develop a design approach called (ASOD). The ASOD consists of agent service-based architectural design and detailed design. ASOD expands the content and range of agent and ABS, and synthesizes the qualities of SOC such as interoperability and openness, and the performances of ABC like flexibility and autonomy. The above techniques have been deployed in developing an online trading and mining support infrastructure F-Trade.

Idea of networked software agents is particularly popular in the field of information sciences dealing with distributed content, whereas in industrial automation its use is usually limited to manufacturing systems. This work presents a concept of multi–agent networked system for automation of continuous processes. Some properties of typical software agent (i.e. advanced high level languages and social skills) had to be dropped in exchange for determinism and satisfying time performance in negotiations between network–connected control components. The proposed environment is National Instruments LabVIEW, very popular solution for automation and measurement. LabVIEW is equipped with advanced data acquisition tools and is capable of artificial intelligence methods, although it lacks agentification mechanisms. A framework is presented, providing LabVIEW the required functionality.

Some research has been done on probabilistic extension of description logics such as P-CLASSIC and P- which focus on the statistical information. For example, in those kind of probabilistic DL, we can express such kind of uncertainty that the probability a chosen individual in concept is also in concept is 90 percent. This kind of statistical knowledge is certain which means the author of this statement is sure about it. In this paper, we will describe a new kind of probabilistic description logic Pr which could let user express the uncertain knowledge(i.e. degrees of belief). For example, if the user is not sure about that concept is subsumed by concept , he could describe it with Pr such as the probability that concept is subsumed by concept is 90 percent.Furthermore, user could make use of the uncertain knowledge to infer some implicit knowledge by the extension of of which will be also introduced in this paper.

In recent years, it becomes vigorous to forecast a market by using machine learning methods. Since they assume that each trader’s individual decisions do not affect market prices at all, most existing works use a past market data set. Meanwhile there is an attempt to analyze economic phenomena by constructing a virtual market simulator, where human and artificial traders really make trades. Since prices in the market are determined by every trader’s decisions, it is more realistic and the assumption cannot be applied any more. In this work, we design and evaluate several reinforcement learners on a futures market simulator U-Mart (Unreal Market as an Artificial Research Testbed). After that, we compare our learner to the previous champions of U-Mart competitions.

The asynchronous searching techniques are characterized by the fact that each agent instantiates its variables in a concurrent way. Then, it sends the values of its variables to other agents directly connected to it by using messages. These asynchronous techniques have different behaviors in case of delays in sending messages. This article depicts the opportunity for synchronizing agents’ execution in case of asynchronous techniques. It investigates and compares the behaviors of several asynchronous techniques in two cases: agents process the received messages asynchronously (the real situation from practice) and the synchronous case, when a synchronization of the agents’ execution is done i.e. the agents perform a computing cycle in which they process a message from a message queue. After that, the synchronization is done by waiting for the other agents to finalize the processing of their messages. The experiments show that the synchronization of the agents’ execution leads to lower costs in searching for solution. A solution for synchronizing the agents’ execution is proposed for the analyzed asynchronous techniques.

Several recent investigations in the field of multiple agent systems have shown interest in the problem of regulating agents’ behaviour with rules that obeyed by the agents when the agents are not enforced to comply to the rules themselves. Multiple Agent Systems where agents exhibit attitudes with respect to the above mentioned aspects are named Ethical MAS. A family of interesting problems in Ethical MAS are introduced. We then consider two sample interpretations, one based upon simple commitment to the law and to personal interest, and one based on the commitment to , the attention to and the interest in . Based on these three aspects we provide a classification of social attitudes of agents in EMAS.

Currently we don’t have a reliable abstraction for modeling activity in knowledge-processing multiagent systems with evolving metadata. The aim of this paper is to propose an approach to simulation of evolving society of software agents with private vocabularies in form of semantic nets (also: lightweight ontologies). The conditions for successful simulation of this kind of systems are formulated with respect to up-to-day results in research on agents, Semantic Web and network theory. The generic algorithm is proposed and the importance of the presented results for predicting behavior of future autonomous agents’ societies in Web-based environments is discussed.

We have developed one stochastic model for intelligent selection of software components, in Internet. Components can be physically located in different repositories, and the selection is done using a XML file which is associated to each component. This file contains the most relevant characteristics of the component, with one extra field stored to be used by this algorithm; this field is called “pheromone”, which is a concept taken from collective intelligence theory that has been the main inspiration of this work. Swarm intelligence is based on each agent capacity to work individually in order to achieve a collective goal; intelligent agents interact not only with each other but also with their environment. This model can be used not only for component selection but also for services, resources, etc. This is because it is general enough for been replicated with different types of requirements.

In this paper we present a methodology to specify Multiagent Systems, called MASINA. MASINA is based on MAS-CommonKADS; we use the models presented in this methodology to propose some extensions, modifications and substitutions allowing to describe the intelligent characteristics of an agent or group of agents, to use intelligent techniques for the accomplishment of tasks (e.g. artificial neural networks), and to specify emergent coordination approaches, among others.

Sudden changes on road networks, including new roads, bridge construction, road blockage or traffic accidents cause travelers to switch their routes to less costly ones as compared to alternative routes. Travelers, however, tend to take higher cost routes due to insufficient information and errors in perceived travel time. This may cause severe congestion on a certain route. Conventional models, however, are unable to adequately simulate travelers’ behavior under such suddenly changing network conditions. The objective of this paper is to analyze travelers’ daily travel behavior in such cases via a stochastic process, the Markov-chain approach, which is considered to be a suitable method for representing sudden changes in states. This model is based on agent and we assumes that travelers select their route via learning process of travel time that they had previously experienced.