Baryon polarization in low-energy unpolarized meson-baryon scattering

We compute the polarization of the final-state baryon, in its rest frame, in low-energy meson--baryon scattering with unpolarized initial state, in Unitarized BChPT. Free parameters are determined by fitting total and differential cross-section data (and spin-asymmetry or polarization data if available) for $pK^-$, $pK^+$ and $p\pi^+$ scattering. We also compare our results with those of leading-order BChPT

Graphical models for interactive POMDPs: representations and solutions

We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive inf uence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic inf uence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that IPOMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents’ candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance

Argumentative Agents Negotiating on Potential Attacks

When arguing, agents may want to discuss about the details after agreeing about the general problems. We propose to model this kind of situation using an extended argumentation framework with potential attacks. Agents negotiation about raising potential attacks or not, in order to maximize the number of their accepted arguments. The result of the negotiation process consists in the formation of coalitions composed by those agents which have found an agreement. The two proposed negotiation protocols have been implemented and an evaluation, addressed by means of experimental results, shows which combination of strategies and negotiation protocol allows the agents to optimize outcomes

A Framework for Preventive State Anticipation

A special kind of anticipation is when an anticipated undesired situation makes an agent adapt its behavior in order to prevent that this situation will occur. In this chapter an approach is presented that combines low level reactive and high level deliberative reasoning in order to achieve this type of anticipatory behavior. A description of a general framework for preventive state anticipation is followed by a discussion of different possible instantiations. We focus on one such instantiation, linear anticipation, which is evaluated in a number of empirical experiments in both single- and multi-agent contexts

Towards a Taxonomy of Decision Making Problems in Multi-Agent Systems

Abstract. Taxonomies in the area of Multi-Agent Systems (MAS) classify problems according to the underlying principles and assumptions of the agents ’ abilities, rationality and interactions. A MAS typically consists of many autonomous agents that act in highly complex, open and uncertain domains. A taxonomy can be used to make an informed choice of an efficient algorithmic solution to a class of decision making problems, but due to the complexity of the agents ’ reasoning and modelling abilities, building such a taxonomy is difficult. This paper addresses this complexity by placing model representation, acquisition, use and refinement at the centre of our classification. We classify problems according to four agent modelling dimensions: model of self vs. model of others, learning vs. non-learning, individual vs. group input, and competition vs. collaboration. The main contributions are extensions of existing MAS taxonomies, a description of key principles and assumptions of agent modelling, and a framework that enables a choice for an adequate approach to a given MAS decision making problem.