Titlebook: RoboCup 2003: Robot Soccer World Cup VII; Daniel Polani,Brett Browning,Kazuo Yoshida Conference proceedings 2004 Springer-Verlag Berlin He

忍耐

–FER

Complexity Science and Representation in Robot Soccerierarchical structure. One of its main predictive tools is computer-generated distributions of possible future system states. This assumes that the system can be represented inside computers. Robot soccer provides an excellent laboratory subject for complexity science, and we seek a lattice hierarch

空气

出生

Evaluating Team Performance at the Edge of Chaoscision process. The presented quantitative information-theoretic methods measure . and . entropy, and detect phase transitions – the . – in team performance. The techniques clearly identify under-performing states, where a change in tactics may be warranted. This approach is a step towards a unified

性冷淡

hazard

GUMP

Feature-Based Declarative Opponent-Modellingard-code all possible interaction situations into their software, because there are many types of agents to be encountered. Thus, agents have to adapt their behavior online depending on the encountered agents. This paper proposes that agent behavior can be classified by distinct and stable tactical

小丑

Scenario-Based Teamworking, How to Learn, Create, and Teach Complex Plans?of cooperative intelligent agents could be able to execute complex plans in nondeterministic, adversary, and dynamic environments which communication cost is high. The base idea of this method is to define Scenario for different situations. With a graph of scenarios, a team of agents can execute, le

GEAR

Specifying Agent Behaviors with UML Statecharts and StatEdit manner arises. One way of achieving this is by means of a graphical formalism. For using such a formalism the availability of tools, that support a developer, is of great importance. In this paper we present an approach to specifying agent behaviors on different levels of abstraction with the help

逃避责任

Echo State Networks for Mobile Robot Modeling and Control State Networks (ESNs) has made RNN training easy and fast and makes RNNs a versatile tool for many problems. The key idea is training the output weights . of an otherwise topologically unrestricted but contractive network. After outlining the mathematical basics, we apply ESNs to two examples namel