this paper, we discuss these questions and describe some emerging technologies that provide answers. In the final section, we mention some additional issues and summarize the key points of the paper. (For more information on agent-based software engineering, see [Genesereth 1989] and [Genesereth 1992]. See also [Shoham 1993] for a description of a variation of agent-based software engineering known as "agent-oriented programming".) 2. Agent Communication Language

This work presents a generalized theoretical framework that allows incorporation of opponent models into adversary search. We present the M algorithm, a generalization of minimax that uses an arbitrary opponent model to simulate the opponent's search. The opponent model is a recursive structure consisting of the opponent's evaluation function and its model of the player. We demonstrate experimentally the potential benefit of using an opponent model. Pruning in M is impossible in the general case. We prove a sufficient condition for pruning and present the fffi algorithm which returns the M value of a tree while searching only necessary branches. Introduction The minimax algorithm (Shannon 1950) has served as the basic decision procedure for zero-sum games since the early days of computer science. The basic assumption behind minimax is that the player has no knowledge about the opponent's decision procedure. In the absence of such knowledge, minimax assumes that the opponen...

This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We define State Oriented Domains, a general category of interaction. Necessary and sufficient conditions for cooperation are outlined. We use the notion of worth in an altered definition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is offered for conflict resolution, and it is shown that even in a conflict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental conflict might still agree to cooperate up to a certain point). A Unified Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be ...

Agent tracking is an important capability an intelligent agent requires for interacting with other agents. It involves monitoring the observable actions of other agents as well as inferring their unobserved actions or high-level goals and behaviors. This paper focuses on a key challenge for agent tracking: recursive tracking of individuals or groups of agents. The paper first introduces an approach for tracking recursive agent models. To tame the resultant growth in the tracking effort and aid real-time performance, the paper then presents model sharing, an optimization that involves sharing the effort of tracking multiple models. Such shared models are dynamically unshared as needed --- in effect, a model is selectively tracked if it is dissimilar enough to require unsharing. The paper also discusses the application of recursive modeling in service of deception, and the impact of sensor imperfections. This investigation is based on our on-going effort to build intelligent pilot agents...

. Intelligent agents, interacting with other agents in a changing and uncertain world, must be willing to revise their beliefs and intentions in response to new observed or communicated information. In this paper we present a unified framework for the revision of beliefs and intentions, extending that developed by Galliers [8]. The framework has been fully implemented, and uses a notion of belief focus to enable practical reasoning given resource bounds. It is being tested by modelling human cooperative dialogue fragments. 1. Introduction An agent, acting and interacting in the world, must be able to revise its beliefs and intentions based on new information from the world and from other agents. If that information conflicts with its existing beliefs, the agent must be able to choose whether to believe the new information, and if so, how this should affect the agent's other beliefs and intentions. The way beliefs are revised should depend on (at least) the authority of the source of a...

This paper focuses on event tracking in one complex and dynamic multi-agent environment: the air-combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals three new issues that have not been addressed in previous work in this area: (i) tracking events generated by agents' flexible and reactive behaviors, (ii) tracking events in the context of continuous agent interactions, and (iii) tracking events in real-time. The paper proposes one solution to address these issues. One key idea in this solution is that the (architectural) mechanisms that an agent employs in generating its own flexible and reactive behaviors can be used to track other agents' flexible and reactive behaviors in real-time. A second key idea is the use of a world-centered representation for modeling agent interactions. The solution is demonstrated using an implementation of an automated pilot agent.

While human players adjust their playing strategy according to their opponent, computer programs, which are based on the minimax algorithm, use tha same playing strategy against a novice as against an expert. This is due to the assumption of minimax that the opponent uses the same strategy as the player. This work studies the problem of opponent modelling in game playing. We recursively define a player as a pair of a strategy and an opponent model, which is also a player. A strategy can be determined by the static evaluation function and the depth of search. M , an algorithm for searching game-trees using an n-level modelling player that uses such a strategy, is described and analyzed. We demonstrate experimentally the benefit of using an opponent model and show the potential harm caused by the use of an inaccurate model. We then describe an algorithm, M ffl , for using uncertain models when a bound on the model error is known. Pruning in M is impossible in the general case. W...

The Multi-model search framework generalizes minimax to allow exploitation of recursive opponent models. In this work we consider adding pruning to the multi-model search. We prove a sufficient condition that enables pruning and describe two pruning algorithms, fffi and fffi 1p . We prove correctness and optimality of the algorithms and provide an experimental study of their pruning power. We show that for opponent models that are not radically different from the player's strategy, the pruning power of these algorithms is significant. 1 Introduction The minimax algorithm [21] and its fffi version [12] have served as the fundamental decision procedures for zero-sum games since the early days of computer science. The basic assumption behind minimax is that the player has no knowledge about the opponent's decision procedure. In the absence of such knowledge, minimax assumes that the opponent selects an alternative which is the worst from the player's point of view. However, it is q...

It is generally accepted that an agent needs to build models of other agents in its environment. The content of these models ranges from simple entries, such as agent capabilities, to more complex entries, such as agent intentions, goals, desires, etc. There is the problem that the information stored in these models may not be accurate in terms of matching the actual property of the agent being modelled. When this happens the agent storing the models is said to be deluded about the agent being modelled. This paper discusses our synthesis of ideas on the issue of agent delusion and presents results of some of the work we have carried out in trying to overcome delusion within agent models and in preventing the spread of delusions to other agents ’ models when agents communicate/gossip with each other.