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...

Abstract not found

Abstract not found

In a dynamic and open environment negotiation protocols cannot be known beforehand. We propose a methodology for constructing exible negotiation protocols based on joint actions and dialogue games. We view negotiation as a combination of joint actions. Simple dialogue games that consist of initiatives followed by responses function as `recipes for joint action' from which larger interactions can be constructed coherently.

Agents in a multiagent system may need to share information and services. For this, they need to be able to interleave deliberative planning with execution of actions. The deliberative planning is needed to decide which actions to perform to achieve an objective, whereas execution of some of the actions is needed to make a more informed decision on the other actions and to access services provided by other agents. HITaP is a planner that interleaves planning and execution: using HITaP an agent can, during planning, gather information by either direct inspection of the domain or by #ring queries to other agents and recording their answers. Interleaving planning and execution, as provided by HITaP, plays a crucial role in an agent's ability to construct shared plans with other agents and to manage the negotiation process that leads to agreement with the agent's teammates on these plans. HITaP is implemented and currently used as planning module for agents in the RETSINA multiagent system. These agents cooperate to solve problems in di#erent domains that range from portfolio management to command and control decision support systems. 1 1

Teamwork demands agreement among teammembers to collaborate and coordinate effectively. When a disagreement between teammates occurs (due to failures), team-members should ideally diagnose its causes, to resolve the disagreement. Such diagnosis of social failures can be expensive in communication and computation overhead, which previous work did not address. We present a novel design space of diagnosis algorithms, distinguishing several phases in the diagnosis process, and providing alternative algorithms for each phase. We then combine these algorithms in different ways to empirically explore specific design choices in a complex domain, on thousands of failure cases. The results show that centralizing the diagnosis disambiguation process is a key factor in reducing communications, while run-time is affected mainly by the amount of reasoning about other agents. These results contrast sharply with previous work in disagreement detection, in which distributed algorithms reduce communications.

We propose group communication for agent coordination within “active rooms” and other pervasive computing scenarios featuring strict real-time requirements, inherently unreliable communication, and a large but continuously changing set of context-aware autonomous systems. Messages are exchanged over multicast channels, which may remind of chat rooms in which everybody hears everything being said. The issues that have to be faced (e.g., changing users’ preferences and locations; performance constraints; redundancies of sensors and actuators; agents on mobile devices continuously joining and leaving) require the ability of dynamically selecting the “best” agents for providing a service in a given context. Our approach is based on the idea of implicit organization, which refers to the set of all agents willing to play a given role on a given channel. An implicit organization is a special form of team with no explicit formation phase and a single role involved. No middle agent is required. A set of protocols, designed for unreliable group communication, are used to negotiate a coordination policy, and for team coordination. Preconditions and effects of these protocols are formalized by means of the joint intention theory (JIT).

Abstract. To scale agent technologies for widespread use in open systems, agents must have an understanding of the organizational context in which they operate. In this paper we focus on the issue of task valuation and action selection in socially situated or organized agents – specifically on the issue of quantifying agent relationships and relating work motivated by different sources. 1

this paper by the U.S. Army Research Office under contract/grant number DAAH 04 95 10628 and the U.S. National Science Foundation under grant IRI9311988. Some of the work described above was developed in collaboration with James Allen and supported by ONR/DARPA under grant number N00014-92J -1512, by ONR under research grant number N00014-90-J-1811, and by NSF under grant number IRI-9003841.

. Teams arise in a number of important multiagent applications. Several theories of intentions for teams have been proposed. By and large, these theories tend to model team intentions exclusively on the basis of mental concepts, and fail to acknowledge the internal structure of teams. We present a formal theory of intentions for teams that considers the structure of teams explicitly. In this context, we distinguish between exodeictic and endodeictic intentions, which are conceptualized as pointing outward or inward from a team. These concepts are formalized in a framework that models the structure of teams in terms of their members' commitments and coordination requirements. In this way, our approach combines mental and social concepts in a principled manner. We describe some postulates concerning intentions and structure, and give technical results establishing or falsifying these postulates with different definitions. 1 Introduction Intentions have drawn much attention in multiagent...