Catálogo de publicaciones - libros

Physically based rendering is the process of generating a 2D image from the abstract description of a 3D Scene. Despite the development of various new techniques and algorithms, the computational requirements of generating photorealistic images still do not allow to render in real time. Moreover, the configuration of good render quality parameters is very difficult and often too complex to be done by non-expert users. This paper describes a novel approach called (standing for “”) which utilizes principles and techniques known from the field of multi-agent systems to optimize the rendering process. Experimental results are presented which show the benefits of -based rendering optimization.

Recent years of empirical research have collected enough evidences that for efficient markets the process of lower-wealth accumulation by capital investment is approximated by log-normal and high-wealth range by Pareto wealth distribution. This research aims to construct a simple neural network (NN) based multiagent system of heterogeneous agents’ targeted to get on the efficiency frontier by combining investments to the real life index funds and nonrisky financial assets, diversifying the risk and maximizing the profits. Each agent is represented by the different stock trading strategy according to his portfolio, saving and risk aversion preferences. The goal is, following empirical evidences from the real investment markets, to find enough proofs that NN-based multiagent system, in principle, has the same fundamental properties of real investment markets described by the log-normal, Pareto wealth and Levy stock returns distributions and can be used further to simulate even more complex social phenomena.

The increasing popularity of the “business ecosystem” concept in (business) strategy reflects that it is seen as one way to cope with increasingly dynamic and complex business environments. Nevertheless, the lack of a convincing model of a business ecosystem has led to the development of software which only give organisations a partial aid whilst neglecting their need for adaptation. Research in Multi-Agent Systems has proved to be suitable for modelling interactions among disparate sort of entities such as organisations. On the other hand, natural ecosystems continue to adapt themselves to changes in their dynamic and complex environments. In this paper, we present the Dynamic Agent-based Ecosystem Model. It combines ideas from natural ecosystems and multi-agent systems for business interactions.

We introduce the as a large-scale dilemma. We then present a framework for cooperative consensus formation in large-scale MAS under the . Forming consensus is performed by demonstrating the applicability of a low-complexity physics-oriented approach to a large-scale ad hoc network problem. The framework is based on modeling cooperative MAS by a physics percolation theory. According to the model, agent-systems inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. Specifically, we focus on the percolation theory, the emergence of self-organized criticality, and the exploitation of phase transitions. We provide a detailed low-ordered algorithm to be used by a single agent and implement this algorithm in our simulations. Via these approaches we demonstrate effective message delivery in a large-scale ad hoc network that consists of thousands of agents.

Past years have witnessed a growing interest in automated negotiation as a coordination mechanism for interacting agents. This paper presents a generic, problem- and domain-independent framework for argumentation-based negotiation that covers both essential agent-internal and external components relevant to automated negotiation. This framework, called Negotiation Situation Specification Scheme (N3S), is both suited as a guideline for implementing negotiation scenarios as well as integrating available approaches that address selective aspects of negotiation. In particular, N3S contributes to the state of the art in automated negotiation by identifying and relating basic argument types and negotiation stages in a structured and formal way.

In this paper we investigate the effect of mediated partnerships over agents’ equilibrium strategies in two-sided economic search. A is formed when an agent acts as a mediator, establishing a partnership between a pair of agents it encountered along its search, thereby reducing the other agents’ amount of search. Surprisingly, this reduction in market friction induced by mediated partnerships does not always improve market efficiency. Use of mediated partnerships changes the equilibrium strategies used by agents in two-sided search models and introduces substantial computational complexity. This computational complexity is overcome with an innovative algorithm that facilitates equilibrium calculation.

Coalitions are often required for multi-agent collaboration. In this research, we consider tasks that can only be completed with the combined efforts of multiple agents using approaches which are both cooperative and competitive. Often agents forming coalitions determine optimal coalitions by looking at all possibilities. This requires an exponential algorithm and is not feasible when the number of agents and tasks is large. We propose agents use a two step process of first determining the task, and secondly, the agents that will be solicited to help complete the task. We describe polynomial time heuristics for each decision. We measure four different agent types using the described heuristics. We explore diminishing choices and performance under various parameters.

Coordination systems have been used in a variety of different applications but have never performed well in large scale, faulty settings. The sheer scale and level of complexity of today’s applications is enough to make the current ways of thinking about distributed systems (e.g. deterministic decisions about data organization) obsolete. All the same, computer scientists are searching for new approaches and are paying more attention to stochastic approaches that provide good solutions “most of the time”. The trade-off here is that by loosening certain requirements the system ends up performing better in other fronts such as adaptiveness to failures. Adaptation is a key component to fault-tolerance and tuple distribution is the center of the fault-tolerance problem in tuple-space systems. Hence, this paper shows how the tuple distribution in -like systems can be solved by using an adaptive self-organized approach Swarm Intelligence. The results discussed in this paper demonstrate that efficient and adaptive solutions to this problem can be achieved using simple and inexpensive approaches.

When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conflicts in the behavior of agents can make the execution of interactions more reliable. Such an alignment mechanism will reduce the necessary human intervention. This paper shows how to describe a policy for alignment, which an agent can apply when its behavior is in conflict with other agents. An extension of Petri Nets is used to capture the intended interaction of an agent in a formal way. Furthermore, a mechanism based on machine learning is implemented, to enable an agent to choose an appropriate alignment policy with collected problem information. Human intervention can reinforce certain successful policies in a given context, and can also contribute by adding completely new policies. Experiments have been conducted to test the applicability of the alignment mechanism and the main results are presented here.

Coalition formation in social networks consisting of a graph of interdependent agents allows many choices of which task to select and with whom to partner in the social network. Nodes represent agents and arcs represent communication paths for requesting team formation. Teams are formed in which each agent must be connected to another agent in the team by an arc. Agents discover effective network structures by adaptation. Agents also use several strategies for selecting the task and determining when to abandon an incomplete coalition. Coalitions are finalized in one-on-one negotiation, building a working coalition incrementally.