Although RoboCup's primary objective is a Although RoboCup's final target is a world For the Although it is obvious that building a robot robot (physical robot and software agent) to to play a soccer game is an immense challenge, play a soccer game reasonably well, a wide readers might wonder why we propose range of technologies need to be integrated, RoboCup. It is our intention to use RoboCup and numbers of technical breakthroughs as a vehicle to revitalize AI research by offering must be accomplished. When the accomplishment intelligent robotics, and sensor fusion. of such a goal has significant social The first RoboCup, RoboCup-97, will be held impact, it is considered a grand-challenge during the Fifteenth International Joint Conference project (Kitano et al. 1993). Building a robot on Artificial Intelligence (IJCAI-97) in to play a soccer game itself does not generate Nagoya, Japan, as part of IJCAI-97's special significant social and economic impact, but Accessibility Complete Incomplete and architectures can be evaluated. Computer Sensor Readings Symbolic Nonsymbolic chess is a typical example of the standard Control Central Distributed problem. Various search algorithms were evaluated and developed using this domain. With the recent accomplishment by the Deep Blue team, which beat Kasparov, a human grand Table 1. A major reason for the success of computer chess as a standard problem is that the evaluation of the accomplishment will certainly be considered the progress was clearly defined.

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our goal is to create a mobile agent that can autonomously learn from its environment based on its own actions, percepts, and mis-sions. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODA's software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. The planner uses a map of the environment to determine a sequence of goals to be accomplished by the robot and delegates the detailed executions to the set of behaviors at the lower layer. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODA's performance at the robot competition.

This article was reprinted with permission from "ACM Computing Surveys: 28(4): December 1996, 653-670. original is available from ACM.

Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelligent agent can choose an action in an adversarial environment. We assume that the agent has a specific goal to achieve. We conduct this investigation in a framework where teams of agents compete in a game of robotic soccer. The real system of model cars remotely controlled from off-board computers is under development.

Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelligent agent can choose an action in an adversarial environment. We assume that the agent has a specific goal to achieve. We conduct this investigation in a framework where teams of agents compete in a game of robotic soccer. The real system of model cars remotely controlled from off-board computers is under development.

Topics An all-star panel assembled to pose life, and a wealth of other topics were of the technical papers spanned a "Challenge Problems for Artificial Intelligence."

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 preferred by all agents, even though there exists a rational agreement that would achieve all their goals. Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore stand-alone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one 'strict', the other 'tolerant', and analyze their affects on the stability and efficiency of negotiation outcomes.

A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. Thus, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves to survive decades of autonomous operation. Achieving these large-scale modeling and configuration tasks will require a tight coupling between the higher-level coordination function provided by symbolic reasoning and the lower-level autonomic processes of adaptive estimation and control. To be economically viable, they will need to be programmable purely through high-level compositional models. Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous system architecture that is taking us into the new millennium.

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.

The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented. It is further argued that research on, and the development of, collaborative systems should itself be a collaborative endeavor -- within AI, across subfields of computer science, and with researchers in other fields.