Liz Sonenberg

Planned Team Activity

Agents situated in dynamic environments can be supplied in advance with a repertoire of plans that permit them to rapidly generate appropriate sequences of actions in response to important events. When agents can form teams, new problems emerge regarding the representation and execution of joint actions. In this paper we introduce a language for representing joint plans for teams of agents, we describe how agents can organize the formation of a suitably skilled team to achieve a joint goal, and we explain how such a team can execute these plans to generate complex, synchronized team activity. The formalism provides a framework for representing and reasoning about joint actions in which various approaches to co-ordination and commitment can be explored.

On social commitment, roles and preferred goals

We present a framework for modelling various types of influences on an agents prioritising of its goals, with emphasis on obligations arising from the agents roles in designated relationships. The framework is based on work of Bell and Huang on goal hierarchies and intention revision, and incorporates notions related to social goal adoption and social commitment as discussed by Castelfranchi. We use roles as an abstraction to enable the agent designer to scope the sphere of influence of one agent with respect to another. Roles then provide a way to specify how the agent should balance competing obligations from different relationships, and from tensions between personal preferences and social obligations. This approach seems to integrate well with the use of team plans for implementing collaborative behaviour.

Using interest and transition models to predict visitor locations in museums

Museums offer vast amounts of information, but a visitors receptivity and time are typically limited, providing the visitor with the challenge of selecting the (subjectively) interesting exhibits to view within the available time. Mobile, electronic handheld guides offer the opportunity to improve a visitors experience by recommending exhibits of interest, and adapting the delivered content. The first step in this personalisation process is the prediction of a visitors activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that take into account the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitors next locations in a museum, and an ensemble model that combines the predictions of these models. The three models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.

Situation awareness in intelligent agents: foundations for a theory of proactive agent behavior

This work outlines a computational model of situation awareness. This model serves two purposes. First, it provides a detailed description for our everyday notion of situation awareness. Second, it offers an alternative perspective of looking into the nature and characteristics of proactive behavior in intelligent agent systems. Most of the existing definitions of proactiveness do not possess the relevant details to encourage rigorous studies of enhancing an agents capability to anticipate and deal with foreseeable situations. This work intends to fill this gap by suggesting a working definition of proactiveness, which place additional emphasis on its underpinning cognitive process and its role in directing an agents attention. A brief discussion of using situation awareness as meta-level control to direct a resource-bounded agents attention is also included.

Using ontological and document similarity to estimate museum exhibit relatedness

Exhibits within cultural heritage collections such as museums and art galleries are arranged by experts with intimate knowledge of the domain, but there may exist connections between individual exhibits that are not evident in this representation. For example, the visitors to such a space may have their own opinions on how exhibits relate to one another. In this article, we explore the possibility of estimating the perceived relatedness of exhibits by museum visitors through a variety of ontological and document similarity-based methods. Specifically, we combine the Wikipedia category hierarchy with lexical similarity measures, and evaluate the correlation with the relatedness judgements of visitors. We compare our measure with simple document similarity calculations, based on either Wikipedia documents or Web pages taken from the Web site for the museum of interest. We also investigate the hypothesis that physical distance in the museum space is a direct representation of the conceptual distance between exhibits. We demonstrate that ontological similarity measures are highly effective at capturing perceived relatedness and that the proposed RACO (Related Article Conceptual Overlap) method is able to achieve results closest to relatedness judgements provided by human annotators compared to existing state-of-the art measures of semantic relatedness.

Bargaining and argument-based negotiation: some preliminary comparisons

Argumentation-based techniques are being increasingly used to construct frameworks for flexible negotiation among computational agents. Despite the advancements made to date, the relationship between argument-based negotiation and bargaining frameworks has been rather informal. This paper presents a preliminary investigation into understanding this relationship. To this end, we present a set of negotiation concepts through which we analyse both bargaining and argumentation-based methods. We demonstrate that if agents have false beliefs, then they may make decisions during negotiation that lead them to suboptimal deals. We then describe different ways in which argument-based communication can cause changes in an agent’s beliefs and, consequently, its preferences over contracts. This enables us to demonstrate how the argumentation-based approach can improve both the likelihood and quality of deals.

Motivational Attitudes of Agents: On Desires, Obligations, and Norms

Many papers about agents mention BDI as a reference logical model for agents, but this model does not offer a thorough formal account of the connections between the different modalities of Beliefs, Desires and Intentions. Of course, work such as that of Rao and Georgeff [14] and of Cohen and Levesque [5] has pointed to some specific constraints, but does not offer a complete logical theory that explains all possible connections between, e.g., goals and intentions. Another point of concern often voiced is the long-standing gap between the BDI logical model and practical agent implementations. Judged by its applicability, it might seem that the BDI model is becoming less important, due to the looseness of its connection to practical systems, and because of its failure to guide research into implementation directions in any obviously useful way.

An empirical study of interest-based negotiation

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic based negotiation, no evidence has been provided to confirm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.

On interest-based negotiation

Negotiation is essential in settings where computational agents have conflicting interests and a desire to cooperate. Mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. These can be seen as models of negotiation in which participants focus on their positions. It is argued, however, that if agents focus instead on the interests behind their positions, they may increase the likelihood and quality of an agreement. In order to achieve that, agents need to argue over each others’ goals and beliefs during the process of negotiation. In this paper, we identify concepts that seem essential for supporting this type of dialogue. In particular we investigate the types of arguments agents may exchange about each others’ interests, and we begin an analysis of dialogue moves involving goals.

Exploring congruence between organizational structure and task performance: a simulation approach

Reorganization of the structure of an organization is a crucial issue in multi-agent systems that operate in an open, dynamic environment. Ideally, autonomous agents must be able to evaluate and decide the most appropriate organization given the environment conditions. That is, there is a need for dynamic reorganization of coordination structures. In this paper, we describe how simulation studies could help to determine whether and how reorganization should take place, and present a simulation scenario that can be used to evaluate the congruence, or fit, between organizational structure and task performance. Preliminary results using a simulation environment illustrate how one can explore triggers for reorganization and compare strategies.