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...

Artificial intelligence research falls roughly into two categories: formal and implementational. This division is not completely firm: there are implementational studies based on (formal or informal) theories (e.g., CYC, SOAR, OSCAR), and there are theories framed with an eye toward implementability (e.g., predicate circumscription). Nevertheless, formal /theoretical work tends to focus on very narrow problems (and even on very special cases of very narrow problems) while trying to get them "right" in a very strict sense, while implementational work tends to aim at fairly broad ranges of behavior but often at the expense of any kind of overall conceptually unifying framework that informs understanding. It is sometimes urged that this gap is intrinsic to the topic: intelligence is not a unitary thing for which there will be a unifying theory, but rather a "society" of subintelligences whose overall behavior cannot be reduced to useful characterizing and predictive principles.

In recentyears, embodied cognitiveagents have become a central research focus in Cognitive Science. We suggest that there are at least three aspects of embodiment--- physical, social and temporal---whichmust be treated simultaneously to make possible a realistic implementation of agency. In this paper we detail the ways in whichattention to the temporal embodiment of a cognitiveagent (perhaps the most neglected aspect of embodiment) can enhance the abilityofanagenttoactintheworld, both in itself, and also by supporting more robust integrations with the physical and social worlds.

This paper describes features of the active logic formalism as a tool for implementing mixed initiative intelligent systems. A framework is provided for assessing systems as "mixed-initiative", which is used to explore the relevant features both in the active logic formalism and associated implementations based upon and using that formalism. Active logics were developed as a means of combining the best of two worlds -- inference and reactivity -- without giving up much of either. This requires a special evolvingduring -inference model of time. Active logics are able to react to incoming information (including dialogue utterances by a collaborative partner) while reasoning is ongoing, blending new inputs into its inferences without having to start up a new theorem-proving effort. An implementation of active logic is described, which also includes special features for reasoning about and performing actions. This implementation is also used as the backbone of a dialogue system. 1 Introduc...

Formal real-time imagination is a term that may curiously describe the activities of a commonsense agent in a real-time setting in general, and in a tight deadline situation in particular. We briefly describe an `active-logic' mechanism that fits this description. Temporal projection is an essential component of realtime planning. We draw a parallel between imagination as we understand it in human context and the capacity of the automated agent to formulate mental images of possible scenarios and plans of action in the course of its reasoning. We outline a treatment of temporal issues of significance to a time-situated reasoning mechanism in a dynamic setting with deadlines. The Yale shooting problem is a benchmark problem in temporal reasoning. We demonstrate how the active-logic planning mechanism successfully handles some interesting real-time variants of the Yale shooting problem. The solutions to each of these illustrate the agent's ability to form contexts within which to reason,...

In this paper I propose a flexible method of characterizing a test environment such that its environmental complexity, information density, variability and volatility can be easily measured.

Real agents must work within the limitations or bounds imposed by their environment and by their own makeup. Among the resources available only in limited quantities are time, space, and information. Most formal attempts to model agents assume that an agent is able to reason forever in a timeless present as if the world had stopped for the agent's benefit. They also assume that the agent is not aware of external events and that his knowledge base is consistent. This work is intended to narrow the gap between formal models for agents and realistic agents. We consider the following issues: limitation of reasoning, awareness of external events and the possibility of inconsistency of knowledge bases. The semantics we use are based upon the Montague semantics. We present a sound and complete framework for a bounded agent with these properties.

The problem of nding a suitable formal approach to describe on-going reasoning process has been open since the very beginning of AI. In this paper we argue that active logic might be a formalism useful in this context. Active logic is rst introduced, then we analyse resource limitations that constrain the space of possible practical realisations of such reasoners. Finally some steps towards creating a practical active logic reasoner are presented. 1