忍耐
发表于 2025-3-23 12:27:32
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–FER
发表于 2025-3-23 17:06:07
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
发表于 2025-3-23 18:10:59
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出生
发表于 2025-3-23 22:37:30
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
发表于 2025-3-24 06:18:26
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hazard
发表于 2025-3-24 09:16:39
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GUMP
发表于 2025-3-24 12:56:37
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
发表于 2025-3-24 15:06:19
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
发表于 2025-3-24 22:06:25
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
发表于 2025-3-25 02:00:20
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