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If we imagine a dynamic environment whose behavior may change in time we can figure out the difficulties that agents located there will have trying to solve problems related to this environment. Changes in an environment e.g. a market, can be quite drastic: from changing the dependencies of some products to add new actions to build new products. The agents should try to cooperate or compete against...
The computation of a Nash equilibrium of a game is a challenging problem in artificial intelligence. This is because the computational time of the algorithms provided by the literature is, in the worst case, exponential in the size of the game. In this paper, we present, to the best of our knowledge, the first anytime algorithm based on the combination of support enumeration methods and local search...
In human-level simulations, like video games can be, the design of character's behaviors has an important impact on simulation realism. We propose to divide it into a reasoning part, dedicated to a planner, and an individuality part, assigned to an action selection mechanism. Applying the separation of declarative and procedural aspects, the principle is to provide every character's agent with the...
Organizational mechanisms can be introduced in a multi-agent system with the aim of influencing the behavior of agents to achieve their objectives in a proper way. We propose to model organizational mechanisms by means of artifacts, which present good advantages for coordinating agents environments. We claim that artifacts, as reactive entities located into the environment of a Multi-agent System,...
This paper investigates the behavior of users judging the similarity of documents from the viewpoint of user feedback cost, in particular judgment time and accuracy. An experiment is conducted, in which 21 test participants were asked to judge the similarity of documents. As the clue for the judgment, 3 types of information: original text, snippet, and term, are mutually provided. The judgment accuracy...
Since several years, great distribution firms implement more and more complex layout and shelf allocation strategies, so as to force empirical know-how to combine with Artificial Intelligence tools. Thus simulation has become an essential tool for designing efficient article layouts. Mathematical models based on statistical observations have been replaced by agent-based models. In this paper we argue...
Significant increase in collected data for investigative tasks and the increased complexity of the reasoning process itself have made investigative analytical tasks more challenging. These tasks are time critical and typically involve identifying and tracking multiple hypotheses; gathering evidence to validate the correct hypotheses and eliminating the incorrect ones. In this paper we specifically...
In this article we describe the implementation of a diversified investment strategy using 25 intelligent agents. Each agent utilizes several data mining models and other artificial intelligence techniques to autonomously day trade an American stock. The agents were individually tested with out-of-sample data corresponding to the period between February of 2006 and June of 2010, and most achieved an...
Group modeling is still an open challenge problem in pedestrian crowd simulations. Most existing work is based on socio-psychological models which can only describe the dynamics of pedestrians' socio-psychological states. The ability of dynamic grouping also requires that pedestrians are intelligent to behave adaptively in the ever changing environment. However, little work has incorporated the effect...
Auctions are well-studied mechanisms to solve decentralized resource allocation problems. In situations where complementarity exists among resources, combinatorial auctions may be utilized. Unfortunately, combinatorial auctions often incur hefty computational requirements, in that the underlying winner determination problem is known to be NP-hard. Although a number of efficient implementations are...
An increasing number of people are socializing within online networks. By means of interaction, network members influence one another's opinion. For companies, it is important to know how opinions spread throughout networks in order to be able to take appropriate marketing actions. A new approach is presented which simulates the spread of opinions within online social networks. The principles of opinion...
Applying distributed constraint optimization problem (DCOP) solution techniques to domains such as service-oriented agent networks can violate key limiting assumptions behind standard DCOP formulations. We extend the multi-constrained (MC-) DCOP to model problems where each agent controls multiple variables, calling this multi-variable (MV-) MC-DCOP. The MV-MC-DCOP formulation abstracts away some...
This paper presents a successful attempt at evolving web intelligence in the tourism scenario, namely throughout two main areas: User Modeling and Recommender Systems. The first subject deals with the correct modeling of tourists' profiles using a wide variety of techniques, such as stereotypes, keywords and psychological models. These techniques, besides presenting user interests with great coherence...
It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which...
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