The original formulation of SharedPlans (Grosz and Sidner, 1990a) was developed to provide a model of collaborative planning in which it was not necessary for one agent to have intentions-to toward an act of a different agent. Unlike other contemporaneous approaches (Searle, 1990), this formulation provided for two agents to coordinate their activities without introducing any notion of irreducible joint intentions. However, it only treated activities that directly decomposed into single-agent actions, did not address the need for agents to commit to their joint activity, and did not adequately deal with agents having only partial knowledge of the way in which to perform an action. This paper provides a revised and expanded version of SharedPlans that addresses these shortcomings. It also reformulates Pollack's definition of individual plans (Pollack, 1990) to handle cases in which a single agent has only partial knowledge; this reformulation meshes with the definition of Shar...

In a multi-agent environment, where self-motivated agents try to pursue their own goals, cooperation cannot be taken for granted. Cooperation must be planned for and achieved through communication and negotiation. We present a logical model of the mental states of the agents based on a representation of their beliefs, desires, intentions, and goals. We present argumentation as an iterative process emerging from exchanges among agents to persuade each other and bring about a change in intentions. We look at argumentation as a mechanism for achieving cooperation and agreements. Using categories identified from human multi-agent negotiation, we demonstrate how the logic can be used to specify argument formulation and evaluation. We also illustrate how the developed logic can be used to describe different types of agents. Furthermore, we present a general Automated Negotiation Agent which we implemented, based on the logical model. Using this system, a user can analyze and explore differe...

An agent's ability to understand an utterance depends upon its ability to relate that utterance to the preceding discourse. The agent must determine whether the utterance begins a new segment of the discourse, completes the current segment, or contributes to it. The intentional structure of the discourse, comprised of discourse segment purposes and their interrelationships, plays a central role in this process (Grosz and Sidner, 1986). In this thesis, we provide a computational model for recognizing intentional structure and utilizing it in discourse processing. The model specifies how an agent's beliefs about the intentions underlying a discourse affects and are affected by its subsequent discourse. We characterize this process for both interpretation and generation and then provide specific algorithms for modeling the interpretation process. The collaborative planning framework of SharedPlans (Lochbaum, Grosz, and Sidner, 1990; Grosz and Kraus, 1993) provides the basis for our model ...

Automated intelligent agents inhabiting a shared environmentmust coordinate their activities. Cooperation { not merely coordination { may improve the performance of the individual agents or the overall behavior of the system they form. Research in Distributed Arti cial Intelligence (DAI) addresses the problem of designing automated intelligent systems which interact e ectively. DAI is not the only eld to take on the challenge of understanding cooperation and coordination. There are a variety of other multi-entity environments in which the entities coordinate their activity and cooperate. Among them are groups of people, animals, particles, and computers. We argue that in order to address the challenge of building coordinated and collaborated intelligent agents, it is bene cial to combine AI techniques with methods and techniques from a range of multi-entity elds, such as game theory, operations research, physics and philosophy. To support this claim, we describe some of our projects, where we have successfully taken an interdisciplinary approach. We demonstrate the bene ts in applying multi-entity methodologies and show the adaptations, modi cations and extensions necessary for solving the DAI problems.

Coordination, the process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner, is perhaps the key problem of the discipline of Distributed Artificial Intelligence (DAI). In order to make advances it is important that the theories and principles which guide this central activity are uncovered and analysed in a systematic and rigourous manner. To this end, this paper models agent communities using a distributed goal search formalism, and argues that commitments (pledges to undertake a specific course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in all DAI systems. 1. The Coordination Problem Participation in any social situation should be both simultaneously constraining, in that agents must make a contribution to it, and yet enriching, in that participation provides resources and opportunities which would otherwise ...

The original formulation of SharedPlans [Grosz and Sidner, 1990] was developed to provide a model of collaborative planning in which it was not necessary for one agent to have intentions toward an act of a different agent. This formulation provided for two agents to coordinate their activities without introducing any notion of jointly held intentions (or, 'weintentions'). However, it only treated activities that directly decomposed into single agents actions. In this paper we provide a revised and expanded version of SharedPlans that accommodates actions involving groups of agents as well as complex actions that decompose into multi-agent actions. The new definitions also allow for contracting out certain actions, and provide a model with the features required in Bratman's account of shared cooperative activity [Bratman, 1992]. A reformulation of the model of individual plans that meshes with the definition of SharedPlans is also provided. 1

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Complex, real-world domains require a rethinking of traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment requires a continual approachinwhich planning and execution are interleaved, there may be uncertaintyin the current and projected world state, and replanning may be required when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, whichweterm distributed,continual planning (DCP). We argue that developing DCP systems will be necessary in order for planning applications to be successful in these environments. We give a historical overview of research leading up to the current state of the art in DCP, and describe research in distributed and continual planning. The increasing emphasis on r...

This paper presents a high-level framework for analysing and designing intelligent agents. The framework's key abstraction mechanism is a new computer level called the Social Level. The Social Level sits immediately above the Knowledge Level, as defined by Allen Newell, and is concerned with the inherently social aspects of multiple agent systems. To illustrate the working of this framework, an important new class of agent is identified and then specified. Socially responsible agents retain their local autonomy but still draw from, and provide resources to, the larger community. Through empirical evaluation, it is shown that such agents produce both good system-wide performance and good individual performance. 1. INTRODUCTION The number of multi-agent systems being designed and built is rapidly increasing as software agents gain acceptance as a powerful and useful technology for solving complex problems (Chaib-draa, 1995; Jennings, 1994; PAAM, 1996). As applications become more comple...

this paper is inspired by philosophical work, it is squarely motivated by the concerns of building intelligent systems that are capable of collaborative behavior, either with a user, or with other such systems. Still, we hope that the paper sheds light on philosophical issues, and treats the subject of joint action at a sufficiently precise level to be illuminating of problems that any philosophical account needs to confront. An important consequence of focusing on joint actions, rather than solely on individual actions, is the opportunity to rethink related theories. In particular, we claim that speech act theory will need to be recast in light of joint action theory since many of the basic illocutionary acts (e.g., requests, promises) are intimately involved in eatablishing, monitoring, and discharging joint activities. However, despite this tight relationship, no existing speech act theory provides guidance on this connection. This paper takes a first step in the direction of linking speech act theory and joint action theory by showing how various speech acts can be used to form and disband teams. It is by now commonplace to observe that joint action is different from a collection of individual actions, even if they are coordinated. Agents can be acting in a coordinated fashion, as in ordinary automobile traffic, but not be acting together. Conversely, agents can be acting together, but not be coordinated except at the start and end of their joint action (e.g, see [36]) The key property distinguishing joint or collaborative action from mere coordinated action is the joint mental state of the participants. The best way to explore what this mental state must be is to imagine a joint action going astray. Our favorite example is driving in a convoy, versus ordinary traff...