In this article, we examine how the research about joint action (Sebanz, Knoblich, Bekkering...), can be intertwisted to the ideas about distributed coordination in uncertain multiagent systems (Maheswaran, Szekely, Rogers, Sánchez...)."Joint action can be regarded as any form of social interaction whereby two or more individuals coordinate their actions in space and time to bring about a change in the environment" (Sebanz, Bekkering, Knoblich, 2006, p. 70). According to the authors, successful joint action depends on the abilities to share representations, to predict actions and to integrate predicted effects of own and other´s actions. A mechanism for sharing representations of objects and events is to direct one´s attention to where an interaction partner is attending. However, a more direct mechanism is provided by action observation; studies about mirror neurons show that during observation of an action, a corresponding representation in the observer´s action system is activated. On the other hand, an efficient means to predict other´s actions that is not based on action observation is knowing what another´s task is. A series of recent studies has shown that individuals form shared representations of tasks quasiautomatically, even when it is more effective to ignore one another. But how individuals adjust their actions to those of another person in time and space can not be explained just by the assumption that representations are shared; action coordination is achieved by integrating the "what" and "when" of others´actions in one´s own action planning. This affects the perception of object affordances, and permits joint anticipatory action control. Very recently, Maheswaran, Rogers and Sanchez (2007) introduce the idea of distributed coordination in multiagent systems. I believe that the research by Sebanz and others in humans can be applied successfully to the area of distributed Artificial Intelligence. Centralized systems can generate fully coordinated policies but put a very high computational load on a single agent. Given a team of agents, every agent in the team has a set of activities that it can perform. Each activity has probabilistic outcomes. Only the agent has current knowledge of its policy at all times. The team reward is a function of the qualities of all activities, and the agents´ objective is to maximize this reward at some terminal time. One way this function can be composed is with a tree where the activities are leaf nodes. Each non-leaf node is associated with an "ancestral" operator which takes the qualities of its children as input. The output of the root node is the team reward function. An agent´s subjective view of the reward function can be defined. It is considered the case where each agent see all ancestral nodes of activities they own, and any nodes and links that connect to its activities and their ancestral nodes via directional operators. The authors introduce distributed coordination between artificial agents, that is, something like the joint action in humans studied by Sebanz and others in humans. In an immediate future, a fertile cross will happen between these two roads.
sábado, 17 de enero de 2009
jueves, 1 de enero de 2009
Winter School 2009: Social Structures in Communication Networks
On 5-10 January 2009 and chaired by Jorge Louça, has been celebrated at the University of Lisboa, the Winter School 2009: "Social Structures in Communication Networks". On Monday, Professor Louça presented the program and scientific goals in the context of the International Doctoral Program in Complexity Sciences. The "Universidade" is accomplishing a great effort following the way pioneered by Santa Fe Institute or the Complex Science Society at Paris.
Professor Araújo analysed whether biological-inspired network models contribute to the understanding of aggregate economic behaviors. Besides, and following the ideas by John von Neumann, she explained the redundant nature rather than efficient nature of the network structures, contributing to clarify the idea of efficiency versus reliability in social environments. In the evening, Professors Louça and Rodrigues introduced tools for the study of social networks like NWB, PAJEK, GUESS or UCINET.
On Tuesday, Professor Symons introduced in his second presentation, ideas including references to the analysis of emergent properties. Symons offers an alternative to the macro-properties model of emergence. Emergence is not restricted to macro-phenomena, but can appear at the intersection of networks. Objects or agents can be involved in distinguishable systems or networks simultaneously. An agent may participate in social, economic, political and other networks at the same time.
On Wednesday, Professors Louça and Rodrigues showed multi-agent based social simulation by means of tools like NETLOGO, MASON or BREVE, while on Thursday, both Professors outlined community detection in social networks, employing algorithms like Girvan-Newmann. In the evening, Professor Palla exposed the statistical properties of community evolution in complex networks. On Friday, Professor Marinheiro spoke about network architectures and organization. Network maps have been used to mitigate the problem of the understanding of the network structures. Professor Lopes explained that in the networked multimedia realm, "more is different".
Finally, on Saturday, 10th, it was programmed a keynote talk by James Sterbenz (University of Kansas), concerning adaptive computer network architectures.
I am very grateful to the University of Lisboa by the organization of this School in which young students and prestigious Professors, have shared very interesting viewpoints about the formal and social analysis of networks, mixing complex systems and dynamics of networks.
"The brain is a network of neurons; organizations are people networks; the global economy is a network of national economies, which are networks of markets...How do such networks matter?"
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