We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area of game theory, but contains new elements that lead to a different solution concept. The advantage of our solution method is that always yields the optimal solution, which is the rational action of the agent in a multi-agent environment, given the agent's state of knowledge and its preferences, and that it works in realistic cases when agents have only a finite amount of information about the agents they interact with. Introduction Since its initial conceptual development several years ago (Gmytrasiewicz, Durfee, & Wehe 1991a; 1991b), the Recursive Modeling Method (RMM) has provided a powerful decision-theoretic underpinning for coordination and communication decisionmaking, including dec...

We describe a decision-theoretic method that an autonomous agent can use to model multiagent situations and behave rationally based on its model. Our approach, which we call the Recursive Modeling Method, explicitly accounts for the recursive nature of multiagent reasoning. Our method lets an agent recursively model another agent's decisions based on probabilistic views of how that agent perceives the multiagent situation, which in turn are derived from hypothesizing how that other agent perceives the initial agent's possible decisions, and so on. Further, we show how the possibility of multiple interactions can affect the decisions of agents, allowing cooperative behavior to emerge as a rational choice of selfish agents that otherwise might behave uncooperatively.

This article puts research on socially intelligent agents (SIA) in the broader context of how humans (and other primates) perceive and interact with the social world. Phylogenetic (evolutionary) and ontogenetic (developmental) issues are discussed with respect to the social origin of primate and human intelligence and human culture. Implications for designing artifacts and for the evolvability of human societies are outlined. A theory of empathy is presented that is based on current research on the primate social brain. Research projects that investigate some of these issues are reviewed. I argue that Socially Intelligent Agents (SIA) research, although strongly linked to software and robotic engineering, goes beyond a software engineering paradigm: it can potentially serve as a paradigm for a science of social minds. A systematic and experimental investigation of human social minds and the way humans perceive the social world can result in truly social artifacts,...

This article discusses the problems and difficulties, the results so far, and some improvements in logic and logical languages that may be required to formalize common sense. Fundamental conceptual advances are almost certainly required. The object of the paper is to get more help for AI from philosophical logicians. Some of the requested help will be mostly philosophical and some will be logical. Likewise the concrete AI approach may fertilize philosophical logic as physics has repeatedly fertilized mathematics.

We address the issue of rational communicative behavior among autonomous self-interested agents that have to make decisions as to what to communicate, to whom, and how. Following decision theory, we postulate that a rational speaker should design a speech act so as to optimize the benefit it obtains as the result of the interaction. We quantify the gain in the quality of interaction in terms of the expected utility, and we present a framework that allows an agent to compute the expected utilities of various communicative actions. Our framework uses the Recursive Modeling Method as the specialized representation used for decision-making in a multi-agent environment. This representation includes information about the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents' beliefs, and so on. Decision-theoretic pragmatics of a comm...

Artificial agents, subsuming both robots and software agents, represent a new paradigm in software engineering and Artificial Intelligence. Depending on the technologies used in their implementation, they may exhibit various skills; in particular, they may act more or less autonomously, they may be able to learn and to adapt to a changing environment, and they may be able to pursue their goals pro-actively. An artificial agent is called rule-based, if its behaviour and/or its knowledge is expressed by means of rules. In this paper, we discuss a general architecture for rule-based agents and how it can be realized with the help of Semantic Web languages. We also show how such agents can go live on the Web by presenting an implementation in Mandarax, a Java rule platform. The concept and implementation are complemented by a running example, the portfolio agent.

.................................................................................................................2 Introduction: The "Solitary Fungus Eater" .................................................................2 The concept of emotion .............................................................................................3 Definition, issues, controversies.....................................................................3 AI models of emotion ..............................................................................................4 "Reasoners"................................................................................................4 Psychological models ...................................................................................5 An example: FEELER ..................................................................................5 Problems with AI models of emotion ........................................................................6 1. The stu...

We address the issue of rational communicative behavior among autonomous intelligent agents that have to make decisions as to what, to whom, and how to communicate. We treat communicative actions as aimed at increasing the efficiency of interaction among agents. We postulate that a rational speaker design a speech act so as to maximally increase the benefit obtained as the result of the interaction. We quantify the gain in the quality of interaction as the expected utility, and we present a framework that allows an agent to compute the expected utility of various communicative actions. Our framework uses the Recursive Modeling Method as the representation of the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents ' beliefs, and so on. A decision-theoretic pragmatics of a communicative act can be then defined as the transformation it induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of the interaction, expressed in terms of the benefit to the agent. We analyze decision-theoretic pragmatics of a number of important communicative acts, and investigate their expected utility using examples.

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, ...

A fundamental feature of effective distributed systems is that the entities comprising the system have some set of guidelines---some plan to follow---that leads them into making good decisions about what to communicate and when. Traditionally, these protocols for communication have been given to the entities at the time that they are designed. For example, knowledgebased entities (agents) have been designed with protocols that allow them to make deals, allocate tasks, negotiate over solutions, and so on. Such distributed systems, however, will be brittle if the agents ever need to go beyond the pre-existing protocol. To constitute a robust system, the agents would benefit from the ability to discover new ways of communicating, and to generalize these into new protocols. This paper extends the recursive modeling method to address issues of embedded communications--- communications occurring in a larger context of other physical and/or communicative activities, and describes how behavior...