We believe user experience in complex work domains is shaped by the effectiveness of technology in jointly accomplishing work goals. Function allocation between humans and smart technology is an important part of effectiveness, in turn, an important contributor to user experience. We study operation of equipment for the International Space Station, using procedure automation with flexible function allocation. We discuss automation goals, their impact on suitable function allocation, and the role of flexible function allocation. We offer examples and propose guidelines.
We propose a parallel tree search algorithm based on the idea of tree-decomposition in which different processors search different parts of the tree. This generic algorithm effectively searches irregular trees using an arbitrary number of processors without shared memory or centralized control. The algorithm is independent of the particular type of tree search, such as single-agent or two-player game, and independent of any particular processor allocation strategy. Uniprocessor depth-first and breadth-first search are special cases of this generic algorithm. The algorithm has been implemented for alpha-beta search in the game of Othello on a 32-node Hypercube multiprocessor.
Bei, Xiaohui (Tsinghua University) | Chen, Ning (Nanyang Technological University) | Hua, Xia (Nanyang Technological University) | Tao, Biaoshuai (Nanyang Technological University) | Yang, Endong (Nanyang Technological University)
We consider the classic cake cutting problem where one allocates a divisible cake to n participating agents. Among all valid divisions, fairness and efficiency (a.k.a. ~social welfare) are the most critical criteria to satisfy and optimize, respectively. We study computational complexity of computing an efficiency optimal division given the conditions that the allocation satisfies proportional fairness and assigns each agent a connected piece. For linear valuation functions, we give a polynomial time approximation scheme to compute an efficiency optimal allocation. On the other hand, we show that the problem is NP-hard to approximate within a factor of Ω 1/√ n for general piecewise constant functions, and is NP-hard to compute for normalized functions.
Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and specific times, and are allocated tasks to optimize given team objectives. We propose an efficient, satisficing and centralized Monte Carlo TreeSearch based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and finds optimal solutions for some non-trivial data sets.