Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication in domains with unreliable, single-channel, low-bandwidth communication. First, the novel team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequent situations. Second, the novel communication method is designed for use during the lowcommunication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated.

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team synchronization domains, as time-critical environments in which agents act autonomously with limited communication, but they can periodically synchronize in a full-communication setting. We present a team agent structure that allows for an agent to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defininga set of roles. Homogeneous agents

This thesis describes the incremental development and main features of a synthetic multi-agent system called UvA Trilearn 2001. UvA Trilearn 2001 is a robotic soccer simulation team that consists of eleven autonomous software agents. It operates in a physical soccer simulation system called soccer server which enables teams of autonomous software agents to play a game of soccer against each other. The soccer server provides a fully distributed and real-time multi-agent environment in which teammates have to cooperate to achieve their common goal of winning the game. The simulation models many real-world complexities such as noise in object movement, noisy sensors and actuators, limited physical abilities and restricted communication. This thesis addresses the various components that make up the UvA Trilearn 2001 robotic soccer simulation team and provides an insight into the way in which these components have been (incrementally) developed. Our main contributions include a multi-threaded three-layer agent architecture, a flexible agent-environment synchronization scheme, accurate methods for object localization and velocity estimation using particle filters, a layered skills hierarchy, a scoring policy for simulated soccer agents and an e#ective team strategy. Ultimately, the thesis can be regarded as a handbook for the development of a complete robotic soccer simulation team which also contains an introduction to robotic soccer in general as well as a survey of prior research in soccer simulation. As such it provides a solid framework which can serve as a basis for future research in the field of simulated robotic soccer. Throughout the project UvA Trilearn 2001 has participated in two international robotic soccer competitions: the team reached 5th place at the German ...

Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. This paper presents the CS Freiburg team, the winner in the middle size league at RoboCup 1998, 2000 and 2001. The paper focuses on multi-agent coordination for both perception and action. The contributions of this work are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment, a rich set of basic skills allowing to respond to large range of situations in an appropriate way, an action selection method based on behavior networks as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multi-robot group, which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multi-robot field.

Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe CMUnited, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and implemented algorithms for strategic collaboration between the robots in an uncertain and dynamic environment. The robots can organize themselves in formations, hold specific roles, and pursue their goals. In game situations, they have demonstrated their collaborative behaviors on multiple occasions.

Reactive deliberation is a novel robot architecture that has been designed to overcome some of the problems posed by dynamic robot environments. It is argued that the problem of action selection in nontrivial domains cannot be intelligently resolved without attention to detailed planning. Experimental evidence is provided that the goals and actions of a robot must be evaluated at a rate commensurate with changes in the environment. The goal-oriented behaviours of reactive deliberation are a useful abstraction that allow sharing of scarce computational resources and effective goal-arbitration through inter-behaviour bidding. The effectiveness of reactive deliberation has been demonstrated through a tournament of one-on-one soccer games between real-world robots. Soccer is a dynamic environment; the locations of the ball and the robots are constantly changing. The results suggest that the architectural elements in reactive deliberation are sufficient for real-time intelligent control in ...

The goal of creating an integrated cognitive robot is still only a tantalizing dream. Current artificial intelligence and robotics research is highly divergent with little or no commonality among specialized subfields. New rich task domains are needed to pose the right challenges to extant theories and promote convergence. We propose soccerplaying as such a task since it requires situated robotics, perception, real-time decision making, planning, plan recognition, learning and multirobot coordination and control. The technology to perform real-time vision and build autonomous robots is available; the Dynamite testbed has been built to perform experiments with multiple robots.

We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic soccer both in simulation and with real physical robots. We briefly present these two frameworks emphasizing their different technical challenges. Creating effective members of a team is a challenging research problem. We first address this issue by introducing a team architecture organization which allows for a rich task decomposition between team members. The main contribution of this paper is our recent introduction of an action selection algorithm that allows for a teammate to anticipate the needs of other teammates. Anticipation is critical for maximizing the probability of successful collaboration in teams of agents. We present our team organization architecture and the anticipation algorithm. We show how our contribution applies to the two concrete robotic soccer frameworks. Anticipation was used in both our CMUnited-98 simulator and CMUnited-98 small-robot teams in the RoboCup-98 competition held jointly with ICMAS in July 1998. The two teams are RoboCup world champions each in its own league. Anticipation was one of major differences between our team and the other teams.

Abstract. The Constraint-Based Agent (CBA) framework is a set of tools for designing, simulating, building, verifying, optimizing, learning and debugging controllers for agents embedded in an active environment. The agent and the environment are modelled symmetrically as, possibly hybrid, dynamical systems in Constraint Nets, as developed by Zhang and Mackworth. This paper is a tutorial overview of the development and application of the CBA framework, emphasizing the important special case where the agent is an online constraint-satisfying device. Here it is often possible to verify complex agents as obeying real-time temporal constraint specifications and, sometimes, to synthesize controllers automatically. The CBA framework demonstrates the power of viewing constraint programming as the creation of online constraint-solvers in dynamic environments. 1