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首页> 外文期刊>Journal of vision >Modeling Strategic Optimization Criteria in Spatial Combinatorial Optimization Problems
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Modeling Strategic Optimization Criteria in Spatial Combinatorial Optimization Problems

机译：空间组合优化问题中的战略优化准则建模

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In many real-world route planning and search tasks, humans must solve a combinatorial optimization problem that holds many similarities to the Euclidean Traveling Salesman Problem (TSP). The problem spaces used in real-world tasks differ most starkly from traditional TSP in terms of optimization criteria a?? Whereas the traditional TSP asks participants to connect all of the nodes to produce the solution that minimizes overall path length, real-world search tasks are often conducted with the goal of minimizing the duration of time required to find the target (i.e., the average distance between nodes). Traditional modeling approaches to TSP assume that humans solve these problems using intrinsic characteristics of the brain and perceptual system (e.g., hierarchical structure in the visual system). A consequence of these approaches is that they are not robust to strategic changes in the aforementioned optimization criteria during path planning. To investigate performance in these tasks, 28 participants solved 18 randomly-presented computer-based combinatorial optimization problems with two sets of task instructions, one designed to encourage shortest-path solutions and the other to encourage solutions that minimized the estimated time to find a target hidden among the nodes (i.e., locations). The node distributions were designed to discriminate between these two strategies. In nearly every case, participants were capable of strategically adapting optimization criteria based on instruction alone. These results indicate the importance of modeling cognition in behaviors that are traditionally thought to be driven automatically by perceptual processes. In addition, we discuss computational models that we have developed to produce optimization criteria-specific solutions to these combinatorial optimization problems using a strategic optimization parameter to guide solutions using a single underlying mechanism. Such models have applications in approximating human behavior in real-world tasks.
机译：在许多现实世界中的路线规划和搜索任务中，人类必须解决与欧几里德旅行推销员问题（TSP）有许多相似之处的组合优化问题。就优化标准而言，实际任务中使用的问题空间与传统的TSP截然不同。传统的TSP要求参与者连接所有节点以产生最小化整体路径长度的解决方案，而现实世界中的搜索任务通常以最小化找到目标所需的持续时间（即平均距离）为目标节点之间）。传统的TSP建模方法假设人类使用大脑和感知系统的固有特征（例如，视觉系统中的层次结构）解决了这些问题。这些方法的结果是，它们在路径规划过程中对上述优化标准中的战略更改不可靠。为了调查这些任务的性能，有28名参与者通过两组任务指令解决了18个随机呈现的基于计算机的组合优化问题，一组任务指令旨在鼓励采用最短路径解决方案，而另一组方案则鼓励采用可将估计时间减少到最小的解决方案。隐藏在节点之间（即位置）。节点分布旨在区分这两种策略。几乎在每种情况下，参与者都能够仅根据说明来策略地调整优化标准。这些结果表明，在传统上认为是由感知过程自动驱动的行为中建立认知模型的重要性。此外，我们讨论了开发的计算模型，这些计算模型使用策略优化参数来指导使用单个基础机制的解决方案，从而针对这些组合优化问题产生针对优化准则的解决方案。此类模型可用于逼近实际任务中的人类行为。

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