Catálogo de publicaciones - libros

In this paper, we investigate the link between logics of games and “mentalistic” logics of rational agency, in which agents are characterized in terms of attitudes such as belief, desire and intention. In particular, we investigate the possibility of extending the logics of games with the notion of agents’ intentions (in the sense of Cohen and Levesque’s BDI theory). We propose a new operator (strσ) that can be used to formalize reasoning about outcomes of strategies in game-like scenarios. We briefly discuss the relationship between intentions and goals in this new framework, and show how to capture dynamic logic-like constructs. Finally, we demonstrate how game-theoretical concepts like Nash equilibrium can be expressed to reason about rational intentions and their consequences.

For the successful management of interactions in open multi-agent systems, a social framework is needed to complement a standard semantics and interaction protocols for agent communication. In this paper a rights-based framework in which interaction protocols and conversation policies acquire their meaning is presented. Rights improve interaction and facilitate social action in multi-agent domains. Rights allow agents enough freedom, and at the same time constrain them (prohibiting specific actions). A general framework for agent communication languages (ACLs) is proposed, defining a set of performatives (semantics) and showing why a set of conversation policies to guide agent’s interactions (pragmatics) is needed. Finally, we show how it is possible to model interaction protocols within a rights-based normative open MAS.

We consider the representation and execution of agents specified using . Previous work in this area has provided a basis for the direct execution of agent specifications, and has been extended to allow the handling of agent beliefs, deliberation and multi-agent groups. However, the key problem of has not been tackled. Given that agents work in unknown environments, and interact with other agents that may, in turn, be unpredictable, then it is essential for any formal agent description to incorporate some mechanism for capturing this aspect. Within the framework of executable specifications, formal descriptions involving uncertainty must also be executable. The contribution of this paper is to extend executable temporal logic in order to allow the representation and execution of uncertain statements within agents. In particular, we extend the basis of the temporal framework with a dimension captured by the recently introduced 45 logic. We provide a description of the extended logic, the translation procedure for formulae in this extended logic to an executable normal form, and the execution algorithm for such formulae. We also outline technical results concerning the correctness of the translation to the normal form and the completeness of the execution mechanism.

This paper investigates the notion of subgoals as used in plans in cognitive agent programming languages. These subgoals form an abstract representation of more concrete courses of action or plans. Subgoals can have a procedural interpretation (directly linked to a concrete plan) or a declarative one (the state to be reached as represented by the subgoal is taken into account). We propose a formal semantics for subgoals that interprets these declaratively, and study the relation between this semantics and the procedural subgoal semantics of the cognitive agent programming language 3APL. We prove that subgoals of 3APL can be programmed to behave declaratively, although the semantics is defined procedurally.

We formulate the multi-team formation (M-TF) domain-independent problem and describe a generic solution for the problem. We illustrate the M-TF preference relation component in the domain of a large-scale disaster response simulation environment. The M-TF problem is the precursor of teamwork that explicitly addresses the achievement of several short time period goals, where the work to achieve the complete set of goals overwhelms the working capacity of the team formation space (all teams formed from the finite set of available agents). Decisions regarding team formation are made by the agents considering their own probabilistic beliefs and utility preferences about the whole (known) set of goals to achieve. The RoboCupRescue simulated large-scale disaster domain is used to illustrate the design of the preference relation domain-specific M-TF component.

If the decisions of agents arise from the solution of general unconstrained problems, altruistic agents can implement effective problem transformations to promote convergence to attractors and draw these fixed points toward Pareto optimal points. In the literature, algorithms have been developed to compute optimal parameters for problem transformations in the seemingly more restrictive scenario of uncertain, quadratic games in which an agent’s response is induced by one of a set of potential problems. This paper reviews these developments briefly and proposes a convergent algorithm that enables altruistic agents to relocate the attractor at a point at which all agents are better off, rather than optimizing a weighted function of the agents’ objectives.

In this paper, we look at the Multi-Agent Meeting Scheduling problem where distributed agents negotiate meeting times on behalf of their users. While many negotiation approaches have been proposed for scheduling meetings, it is not well understood how agents can negotiate strategically in order to maximize their users’ utility. To negotiate strategically, agents need to learn to pick good strategies for negotiating with other agents. We show how the approach, introduced by [1] for team plan selection in small-size robot soccer, can be used to select strategies. Selecting strategies in this way gives some theoretical guarantees about regret. We also show experimental results demonstrating the effectiveness of the approach.

This chapter contains papers presented in the workshop on Text Mining and Applications (TeMA 2005), organized in the framework of the Portuguese Association for Artificial Intelligence conference (EPIA). This workshop is aimed at attracting quality papers and enhancing the knowledge in this area.

27 papers were submitted. From these, 9 papers were selected for publication in this Springer volume. These numbers show current importance of this field in AI and suggest that the organization of equivalent events in future EPIA editions should be pursued.

First paper works on bilingual lexical acquisition from non parallel corpora, applicable in Machine Translation. Second paper describes work on Text Summarization. Third uses Transformation Based learning for NP Identification applied to Portuguese.Fourth uses linguistic knowledge for passage retrieval and question answering. Fifth describes the use of weakly supervised learning for extraction of semantic patterns. Sixth paper proposes a method for semantic indexing and evaluates it on traditional Information Retrieval tasks. Seventh works on unsupervised language independent extraction of multi-word terms, applicable in multiple domains and evaluates their results for Slovene and English. Eighth paper presents a variant of a known method for Anaphora resolution, adapted to Portuguese. Last paper proposes a stemmer for Brazilian Portuguese.

Few approaches to extract word translations from non-parallel texts have been proposed so far. Researchers have not been encouraged to work on this topic because extracting information from non-parallel corpora is a difficult task producing poor results. Whereas for parallel texts, word translation extraction can reach about 99%, the accuracy for non-parallel texts has been around 72% up to now. The current approach, which relies on the previous extraction of bilingual pairs of lexico-syntactic templates from parallel corpora, makes a significant improvement to about 89% of words translations identified correctly.

We describe experiments on content selection for producing biographical summaries from multiple documents. The method relies on a set of patterns to identify descriptive phrases, an available co-reference resolution algorithm, and a greedy, corpus-based sentence deletion procedure for document compression. We show that in an automatic evaluation of content using ROUGE, the proposed method obtains very good performance.