The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multiagent collaboration, strategy acquisition, realtime reasoning, robotics, and sensor-fusion. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot, RoboCup is a task for a team of multiple fastmoving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment. 1 Introduction: RoboCup as a Standard AI Problem We propose a Robot World Cup (RoboCup), as a new standard problem for AI an...

Software and knowledge... In this article, we argue that intelligent agents and agent-based systems offer novel opportunities for developing effective tools and techniques. Following a discussion on the classic subject of what makes software complex, we introduce intelligent agents as software structures capable of making "rational decisions". Such rational decision-makers are well-suited to the construction of certain types of software, which mainstream software engineering has had little success with. We then go on to examine a number of prototype techniques proposed for engineering agent systems, including formal specification and verification methods for agent systems, and techniques for implementing agent specifications

Abstract — Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered. I. DISTRIBUTED PROBLEM SOLVING

Introduction Cooperative Distributed Problem-Solving (CDPS) studies how a loosely-coupled network of problem solvers can work together to solve problems that are beyond their individual capabilities. Each problem-solving node in the network is capable of sophisticated problem solving and can work independently, but the problems faced by the nodes cannot be completed without cooperation. Cooperation is necessary because no single node has sufficient expertise, resources, and information to solve a problem, and different nodes might have expertise for solving different parts of the problem. For example, if the problem is to design a house, one node might have expertise on the strength of structural materials, another on the space requirements for different types of rooms, another on plumbing, another on electrical wiring, and so on. Different nodes might have different resources: some might be very fast at computation, others might have connections that speed communication, whil

For distributed sensor network applications, a practical approach to generating complete interpretations from distributed data must coordinate how separate, concurrently-running systems form, exchange, and fuse their individual hypotheses to form consistent interpretations. Partial global planning provides a framework for coordinating multiple AI systems that are cooperating in a distributed sensor network. By combining a variety of coordination techniques into a single, unifying framework, partial global planning enables separate AI systems to reason about their roles and responsibilities as part of group problem solving, and to modify their planned processing and communication actions to act as a more coherent team. Partial global planning is uniquely suited for coordinating systems that are working in continuous, dynamic, and unpredictable domains because it interleaves coordination with action and allows systems to make effective decisions despite incomplete and possibly obsolete i...

A Dissertation Presented by TUOMAS W. SANDHOLM

The Functionally-Accurate, Cooperative (FA/C) paradigm provides a model for task decomposition and agent interaction in a distributed problem-solving system. In this model, agents need not have all the necessary information locally to solve their subproblems, and agents interact through the asynchronous, co-routine exchange of partial results. This model leads to the possibility that agents may behave in an uncoordinated manner. This paper traces the development of a series of increasingly sophisticated cooperative control mechanisms for coordinating agents. They include integrating data- and goal-directed control, using static meta-level information specified by an organizational structure, and using dynamic meta-level information developed in partial global planning. The framework of distributed search motivates these developments. Major themes of this work are the importance of sophisticated local control, the interplay between local control and cooperative control, and the use of s...

A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distribution-caused uncertainty and errors as an integral part of the network problem-solving process. In this approach nodes cooperatively problem solve by exchanging partial tentative results (at various levels of abstraction) within the context of common goals. The approach is especially suited to applications in which the data necessary to achieve a solution cannot be partitioned in such a way that a node can complete a task without seeing the intermediate state of task processing at other nodes. Much of the inspiration for the FA/C approach comes from the mechanisms used in knowledge-based artificial intelligence (AI) systems for resolving uncertainty caused by noisy input data and the use of approximate knowledge. The appropriateness of the FA/C approach is explored in three application domains: distributed interpretation, distributed network traffic-light control, and distributed planning. Additionally, the relationship between the approach and the structure of management organizations is developed. Finally, a number of current research directions necessary to more fully develop the FA/C approach are outlined. These research directions include distributed search, the integration of implicit and explicit forms of control, and distributed planning and organizational self-design. I.

This paper was presented at IJCAI-83. Distributed problem-solving networks provide an interesting application area for meta-level control through the use of organizational structuring. We describe a decentralized approach to network coordination that relies on each node making sophisticated local decisions that balance its own perceptions of appropriate problem-solving activity with activities deemed important by other nodes. Each node is guided by a high-level strategic plan for cooperation among the nodes in the network. The high-level strategic plan, which is a form of meta-level control, is represented as a network organizational structure that specifies in a general way the information and control relationships among the nodes. An implementation of these ideas is briefly described along with the results of preliminary experiments with various network problem-solving strategies specified via organizational structuring. In addition to its application to Distributed Artificial Intelligence, this research has implications for organizing and controlling complex knowledge-based systems that involve semi-autonomous problem solving agents. 1

This paper examines the issues that arise in the control of blackboard systems for applications with large and complicated search spaces by analyzing the evolution of blackboard control architectures. We feel that the issues addressed here apply more generally to AI application domains involving complex multi-dimensional search, in which control knowledge is as important to successful problem solving as domain knowledge. Evolution is viewed largely from the context of the Hearsay-II (HSII) speech understanding system. The appeal of the blackboard model is that it provides great flexibility in structuring problem solving. On the other hand, many of the features that are responsible for this flexibility make effective control difficult because they complicate the process of estimating the expected value of potential actions. Among the key themes in the evolution of blackboard control is the development of mechanisms that support more sophisticated goal-directed reasoning. In the basic co...