ReSpecT语言的创新视角:AA元模型在多智能体系统协调中的应用

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本文探讨了形式ReSpecT语言在多智能体系统(MAS)协调中的创新应用,以一种全新的视角——AA(代理和工件)元模型进行分析。作者指出,在过去的十年里,协调模型和语言在复杂系统的交互管理方面取得了显著进展,尤其是在Web服务、工作流管理系统和MAS等领域。这些成果不仅限于跨学科的应用,而是应该首先解决实际问题,然后根据新的概念框架进行深入挖掘和扩展。 ReSpecT语言原为元组中心的行为编程工具,本文将这一成熟语言与AA元模型相结合,创造出名为AAReSpecT的语言。AA元模型提供了一个理解和协调多智能体行为的框架,强调了代理(Agent)和工件(Artifact)在协调过程中的核心作用。通过这种元模型,文章旨在重新定义和扩展原有的ReSpecT语言,使其能够更有效地处理MAS中的协作和任务分配。 在MAS背景下,传统的协调工件被赋予新的含义,它们不再局限于特定的任务执行,而是作为连接不同智能体、促进信息共享和合作的媒介。文章通过一个具体的实例,展示了如何利用AAReSpecT语言在实际MAS协调场景中进行编程和设计,从而提升系统的灵活性和效率。 此外,作者还提到了协调问题在其他社会科学领域的应用,如社会学、经济学和组织理论,这些领域的研究也常常依赖于有效的协调机制来整合和优化各种活动。通过将ReSpecT语言与AA元模型结合,本文旨在为理解并解决这些复杂系统的交互问题提供一个新颖而强大的工具。 总结来说,这篇论文的重要贡献在于提出了一种创新的视角,即通过AA元模型,对ReSpecT语言进行了重新诠释和扩展,为多智能体系统的协调设计提供了更深层次的理论支持和实践指导。通过这种方式,ReSpecT语言在协调技术领域得到了进一步的拓展,推动了该领域的发展和应用。

3.4 Pair Interaction Feature The interaction pattern between two individuals is encoded by a spatial descriptor with view invariant relative pose encoding. Given the 3D locations of two individual detec- tions zi,zj and two pose features pi,pj, we represent the pairwise relationship using view normalization, pose co-occurrence encoding, semantic compression and a spatial histogram (see Fig. 5 for illustration). The view normalization is performed by rotating the two people in 3D space by θ with respect to their midpoint, making their connecting line perpendicular to the cam- era view point. In this step, the pose features are also shifted accordingly (e.g. if θ = 45‘, shift 1 dimension with a cycle). Then, the co-occurrence feature is obtained by building a 2-dimensional matrix in which each element (r, c) corresponds to min(pi(r), pj (c)). Although the feature is view invariant, there are still elements in the matrix that deliver the same semantic concepts (e.g. left-left and right-right). To reduce such unnecessary variance and obtain a compact representation, we perform another transformation by multiplying a semantic compression matrix Sc to the vector form of the co-occurrence feature. The matrix Sc is learned offline by enumerating all possible configurations of view points and grouping the pairs that are equivalent when rotated by 180 degrees. Finally, we obtain the pair interaction descriptor by building a spatial histogram based on the 3D distance between the two (bin centers at 0.2, 0.6, 2.0 and 6.5 m). Here, we use linear interpolation similarly to contextual feature in Sec. 3.3. Given the interac- tion descriptor for each pair, we represent the interaction feature φxx(xi,xj) using the confidence value from an SVM classifier trained on a dictionary of interaction labels Y.什么意思

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