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    How a cooperative behaviour can emerge from a robot team

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    In this paper, we suggest an hybrid architecture where the deliberative part takes advantages from the reactive one and vice versa, to make a multi-robot system to exhibit some assigned cooperative task. We explain our architecture in terms of schemas and a set of firing conditions. To experiment our approach, we have realized an implementation that tries to exploit the resources of our robot team, that participates to the Middle-size RoboCup tournament. Each individual exhibits both reactive and deliberative behaviors which are needed to perform cooperative tasks. To this aim we have designed each robot to become aware of distinguishing configuration patterns in the environment by evaluating descriptive conditions as macroparameters. These are implemented at reactive level, whereas the deliberative level is responsible of a dynamic role assignment among teammates on the basis on the knowledge about the best behavior the team is able to exhibit at the moment. We have tested our approach successfully during the Middle-size Challenge Competition held in Padua on last RobCup2003

    Making Collective Behaviors to work through Implicit Communication

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    The aim of this paper is to investigate how stigmergic information allow each individual of a group of autonomous robots to take advantages from other individual behaviors. The proposed analysis is based on the roboticle model where sensor data and effector commands are treated as energy exchange between the robot and its environment, eventually populated by other robots. Without explicit communication, the collective behavior of a group of teammates can be forced only if the robot designer makes each robot to become aware of distinguishing configuration patterns in the environment. Usually, the job is accomplished both by evaluating descriptive conditions as macroparameters and an appropriate dynamic role assignment among teammates. Since observed individual behaviors can affect the normal course of operations for each robot propagating to other teammates, we want to address some issues on how a collective behavior is fired and maintained

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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