Goal Inference as Inverse Planning
Goal Inference as Inverse Planning
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目标推断作为逆向规划
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
R. Saxe
中科院分区：
文献类型：
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作者：
Chris L. Baker;J. Tenenbaum;R. Saxe
目标推理作为逆向规划Chris L.贝克，约书亚B。作者声明：Rebecca R. Saxe {clbaker，jbt，saxe}@mit.edu马萨诸塞州理工学院脑与认知科学系摘要许多作者（例如Nichols和斯蒂奇（2003）; Baker，Tenenbaum，and Saxe（2006））认为，已经提出的理性原则的定性定义不足以解释人类目标推理的复杂性.此外，非计算模型的定性预测缺乏与人们的判断进行细粒度比较的分辨率。在这里，我们提出了一个计算版本的这种方法来目标推理，在逆概率规划。人们常说“视觉是逆图形”：视觉感知的计算模型--特别是在贝叶斯传统中--通常是如何从场景（即“图形”）形成图像的因果物理过程，并且在从图像感知场景结构时必须反转该过程。通过类比，在逆向规划中，规划是意图导致行为的过程，并且观察者通过反转代理的规划过程的模型来推断代理的意图，给出代理的代理人的观察结果。与计算机视觉中的许多工作一样，逆向规划框架提供了目标推理的理性分析（安德森，1990）。我们假设，人们的目标依赖计划的直觉理论近似于经济学家和心理学家提出的人类决策的科学模型，并且从反转这个理论中获得的自下而上的信息，给定行为的观察，与目标空间的自上而下的先验知识相结合，以允许从行为中进行目标的理性贝叶斯推理。逆向规划框架包括许多特定的模型，这些模型在它们分配给代理人的责任和期望的复杂性方面有所不同。其他代理的目标空间的先验知识是必要的归纳，在这篇文章中，我们将提出和测试几个模型，不同的目标结构的表示。我们的实验范式测试每个模型在一个简单的空间，我们的模型作出细粒度的预测，广泛的行动轨迹。(Our刺激类似于German等人（1995））。这些刺激中的一些显示了通往显著目标的直接路径，并且具有简单的意图解释。其他刺激表现出更复杂的行为，可能没有简单的内在解释。这些类型的轨迹使我们能够区分不同的替代模型，这些模型在复杂目标结构的表示上有所不同。通过改变轨迹的长度，我们测量了受试者的目标推理如何随着时间的推移而变化，通过引出在线和回溯推理，我们测量了受试者如何随着时间的推移整合信息。为了说明我们提出的模型空间，考虑介绍性的例子。提出的关于婴儿和成人的三个问题中的每一个都擅长于从不完整或模糊的行为序列中推断代理人的目标。我们提出了一个框架，目标推理的基础上逆规划，观察员反转的概率生成模型的目标相关的计划来推断代理的目标。逆向规划框架包括许多特定的模型和代表，我们提出了几个特定的模型，并在两个行为实验中测试他们在线和回顾性目标推理。保留字：心理理论行动理解;贝叶斯推理;一个女人走在街上，突然她停了下来，转身，开始朝相反的方向跑。为什么？为什么？她疯了吗？她是否完成了一件我们不知道的差事（也许是把一封信投进邮箱），然后匆匆赶往她的下一个目标？或者她改变主意了？这些推论来自于将目标归因于女性，并用它们来解释她的行为。成年人是从行为观察中推断代理人目标的专家。这些观察结果通常是模糊的或不完整的，但我们每天都会自信地从这些数据中做出很多次目标推断。发展心理学家已经证明，婴儿也会进行简单形式的目标推理。在使用真人刺激的实验中，伍德沃德发现证据表明，6个月大的婴儿将目标归因于人类演员，并且当随后的目标与旧目标不一致时，看得更长（1998）。Meltzoff（1995）表明，18个月奥尔兹模仿的是人类演员的预期行为，而不是意外行为，Csibra和同事发现的证据表明，婴儿从简单的二维动画中移动物体的不完整投射中推断目标（Csibra，Bir 'o，Ko' os，& Germinal，2003），这两个都表明儿童甚至从不完整的动作中推断目标。目标推理表面上的简单性掩盖了复杂的概率归纳。通常有许多目标在逻辑上与特定背景下的行为一致，其他人行为的明显复杂性引发了一系列令人困惑的解释，但观察者对可能目标的归纳跳跃毫不费力地准确发生。这种归纳的壮举是如何实现的呢？几位哲学家和心理学家提出的一个可能的解决方案是，这些推理是由体现理性原则的直觉代理理论实现的：假设理性代理人倾向于尽可能最佳地实现他们的愿望，给定他们的信念（Dennett，1987; Germinster，N 'adasdy，Csibra，& Bir' o，1995）。然而，在这方面，
Goal Inference as Inverse Planning Chris L. Baker, Joshua B. Tenenbaum & Rebecca R. Saxe {clbaker,jbt,saxe}@mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract many authors (e.g. Nichols and Stich (2003); Baker, Tenen- baum, and Saxe (2006)) have argued that the qualitative de- scriptions of the principle of rationality that have been pro- posed are insufficient to account for the complexities of hu- man goal inference. Further, the qualitative predictions of noncomputational models lack the resolution for fine-grained comparison with people’s judgments. Here, we propose a computational version of this approach to goal inference, in terms of inverse probabilistic planning. It is often said that “vision is inverse graphics”: computational models of visual perception – particularly in the Bayesian tra- dition – often posit a causal physical process of how images are formed from scenes (i.e. “graphics”), and this process must be inverted in perceiving scene structure from images. By analogy, in inverse planning, planning is the process by which intentions cause behavior, and the observer infers an agent’s intentions, given observations of an agent’s behav- ior, by inverting a model of the agent’s planning process. Like much work in computer vision, the inverse planning framework provides a rational analysis (Anderson, 1990) of goal inference. We hypothesize that people’s intuitive the- ory of goal-dependent planning approximates scientific mod- els of human decision making proposed by economists and psychologists, and that bottom-up information from inverting this theory, given observations of behavior, is integrated with top-down prior knowledge of the space of goals to allow ra- tional Bayesian inference of goals from behavior. The inverse planning framework includes many specific models that differ in the complexity they assign to the be- liefs and desires of agents. Prior knowledge of the space of other agents’ goals is necessary for induction, and in this pa- per, we will present and test several models that differ in their representations of goal structure. Our experimental paradigm tests each model with a wide range of action trajectories in a simple space for which our models make fine-grained predic- tions. (Our stimuli resemble those of Gergely et al. (1995)). Some of these stimuli display direct paths to salient goals, and have simple intentional interpretations. Other stimuli display more complex behaviors, which may not have simple inten- tional interpretations. These sorts of trajectories allow us to distinguish between alternative models that differ in their rep- resentation of complex goal structure. By varying the length of the trajectories, we measure how subjects’ goal inferences change over time, and by eliciting both online and retrospec- tive inferences, we measure how subjects integrate informa- tion over time. To illustrate the space of models we present, consider the introductory example. Each of the three queries raised about Infants and adults are adept at inferring agents’ goals from in- complete or ambiguous sequences of behavior. We propose a framework for goal inference based on inverse planning, in which observers invert a probabilistic generative model of goal-dependent plans to infer agents’ goals. The inverse plan- ning framework encompasses many specific models and rep- resentations; we present several specific models and test them in two behavioral experiments on online and retrospective goal inference. Keywords: theory of mind; action understanding; Bayesian inference; Markov Decision Processes Introduction A woman is walking down the street, when suddenly she pauses, turns, and begins running in the opposite direction. Why? Is she crazy? Did she complete an errand unknown to us (perhaps dropping off a letter in a mailbox) and rush off to her next goal? Or did she change her mind about where she was going? These inferences derive from attributing goals to the woman and using them to explain her behavior. Adults are experts at inferring agents’ goals from obser- vations of behavior. Often these observations are ambiguous or incomplete, yet we confidently make goal inferences from such data many times each day. Developmental psychologists have shown that infants also perform simple forms of goal inference. In experiments using live-action stimuli, Wood- ward found evidence that 6-month old infants attribute goals to human actors, and look longer when subsequent behav- ior is inconsistent with the old goal (1998). Meltzoff (1995) showed that 18-month olds imitate intended acts of human actors rather than accidental ones, and Csibra and colleagues found evidence that infants infer goals from incomplete tra- jectories of moving objects in simple two-dimensional anima- tions (Csibra, Bir´o, Ko´os, & Gergely, 2003), both suggesting that children infer goals even from incomplete actions. The apparent ease of goal inference masks a sophisticated probabilistic induction. There are typically many goals logi- cally consistent with an agent’s actions in a particular context, and the apparent complexity of others’ actions invokes a con- fusing array of explanations, yet observers’ inductive leaps to likely goals occur effortlessly and accurately. How is this feat of induction possible? A possible solution, proposed by several philosophers and psychologists, is that these inferences are enabled by an intu- itive theory of agency that embodies the principle of rational- ity: the assumption that rational agents tend to achieve their desires as optimally as possible, given their beliefs (Dennett, 1987; Gergely, N´adasdy, Csibra, & Bir´o, 1995). However,
影响因子：
4
作者：
MELTZOFF, AN
通讯作者：
MELTZOFF, AN