This paper describes an implemented computer program that recognizes the occurrence of simple spatial motion events in simulated video input. The program receives an animated line-drawing as input and produces as output a semantic representation of the events occurring in that animation. This paper suggests that the notions of support, contact, and attachment are crucial to specifying many simple spatial motion event types and presents a logical notation for describing classes of events that incorporates such notions as primitives. It then suggests that the truth values of such primitives can be recovered from perceptual input by a process of counterfactual simulation, predicting the effect of hypothetical changes to the world on the immediate future. Finally, it suggests that such counterfactual simulation is performed using knowledge of naive physical constraints such as substantiality, continuity, gravity, and ground plane. This paper describes the algorithms that incorporate these ideas in the program and illustrates the operation of the program on sample input.

This paper discusses the design space of believable social robots. We synthesise ideas and concepts from areas as diverse as comics design and rehabilitation robotics. First, we revisit the work of the Japanese researcher Masahiro Mori in the context of recent developments in social robots. Next, we discuss work in the arts into comics design, an area which has dealt for decades with the problem of creating believable characters. Finally, in order to illustrate some of the important issues involved we focus on a particular application area: the use of interactive robots in autism therapy, work that is carried out in the Aurora project. We discuss design issues of social robots in the context of `design spaces' and `niche spaces', concepts that have been defined originally for intelligent agent architectures [26] but which, we propose, can be highly valuable for social robotics design. This paper is meant to open up a discussion towards a systematic exploration of design spaces and niche spaces of social robots.

This article puts research on socially intelligent agents (SIA) in the broader context of how humans (and other primates) perceive and interact with the social world. Phylogenetic (evolutionary) and ontogenetic (developmental) issues are discussed with respect to the social origin of primate and human intelligence and human culture. Implications for designing artifacts and for the evolvability of human societies are outlined. A theory of empathy is presented that is based on current research on the primate social brain. Research projects that investigate some of these issues are reviewed. I argue that Socially Intelligent Agents (SIA) research, although strongly linked to software and robotic engineering, goes beyond a software engineering paradigm: it can potentially serve as a paradigm for a science of social minds. A systematic and experimental investigation of human social minds and the way humans perceive the social world can result in truly social artifacts,...

In the future, interactive robots will perform many helpful tasks. In 5 studies, we developed techniques for measuring the richness and content of people's mental models of a robot. Using these techniques, we examined how a robot's appearance and dialogue affected people's responses. Participants had a comparatively rich mechanistic perception of the robot, and perceived it to have some human traits, but not complex human attachment, foibles, or creativity. In study 5, participants who interacted with an extraverted, playful robot versus a more serious, caring robot, developed a richer, more positive mental model of the playful robot but cooperated less with it. Our findings imply different designs for robotic assistants that meet social and practical goals.

Visualizing information in user interfaces to complex, large-scale systems is difficult due to an enormous amount of dynamic data distributed across multiple displays. While graphical representation techniques can reduce some of the cognitive overhead associated with comprehension, current interfaces suffer from the over-use of such representation techniques and exceed the human’s perceptual capacity to efficiently interpret them. New display dimensions are required to support the user in information visualization. Three major issues which are problematic in complex system UI design are identified: representing the nature of change, supporting the cognitive integration of data across disparate displays, and conveying the nature of relationships between data and/ or events. Advances in technology have made animation a viable alternative to static representations. Motion holds promise as a perceptually rich and efficient display dimension but little is known about its attributes for information display. This paper proposes that motion may prove useful in visualizing complex information because of its preattentive and interpretative perceptual properties. A review of animation in current user interface and visualization design and research indicates that, while there is strong intuition about the “usefulness ” of motion to communicate, there

A mathematical analysis is presented that attempts to describe the available visual information about rigid and nonrigid motion and the three-dimensional structure of rigidly moving objects. Unlike other approaches, the analysis is based on the geometric relations among a set of trajectories defined over an extended region of space-time, Two experiments are reported in which observers viewed computer simulations of moving objects and were required to judge whether the observed motion appeared to be rigid or nonrigid. The results suggest that the mathematical limitations of a trajectory-based analysis of visual information are consistent with the perceptual limitations of actual human observers. In the mathematical analysis of visual information, perceptual theorists have tended to assume that the only permissible alterations an object can undergo are rigid displacements—changes in position that preserve an object's size and shape. The reasons

Human cognition is often biased, from judgments of the time of impact of approaching objects all the way through to estimations of social outcomes in the future. We propose these effects and a host of others may all be understood from an evolutionary psychological perspective. In this article, we elaborate error management theory (EMT; Haselton & Buss, 2000). EMT predicts that if judgments are made under uncertainty, and the costs of false positive and false negative errors have been asymmetric over evolutionary history, selection should have favored a bias toward making the least costly error. This perspective integrates a diverse array of effects under a single explanatory umbrella, and it yields new content-specific predictions. Better safe than sorry. (folk wisdom) Nothing ventured, nothing gained. (contradictory folk wisdom) These two wisdoms seem contradictory. The first urges caution, whereas the second reminds us that we have nothing to lose and should throw caution to the

We present a Bayesian framework for explaining how people reason about and predict the actions of an intentional agent, based on observing its behavior. Action-understanding is cast as a problem of inverting a probabilistic generative model, which assumes that agents tend to act rationally in order to achieve their goals given the constraints of their environment. Working in a simple sprite-world domain, we show how this model can be used to infer the goal of an agent and predict how the agent will act in novel situations or when environmental constraints change. The model provides a qualitative account of several kinds of inferences that preverbal infants have been shown to perform, and also fits quantitative predictions that adult observers make in a new experiment. 1

Abstract: In my book How the Mind Works, I defended the theory that the human mind is a naturally selected system of organs of computation. Jerry Fodor claims that ‘the mind doesn’t work that way ’ (in a book with that title) because (1) Turing Machines cannot duplicate humans ’ ability to perform abduction (inference to the best explanation); (2) though a massively modular system could succeed at abduction, such a system is implausible on other grounds; and (3) evolution adds nothing to our understanding of the mind. In this review I show that these arguments are flawed. First, my claim that the mind is a computational system is different from the claim Fodor attacks (that the mind has the architecture of a Turing Machine); therefore the practical limitations of Turing Machines are irrelevant. Second, Fodor identifies abduction with the cumulative accomplishments of the scientific community over millennia. This is very different from the accomplishments of human common sense, so the supposed gap between human cognition and computational models may be illusory. Third, my claim about biological specialization, as seen in organ systems, is distinct from Fodor’s own notion of encapsulated modules, so the limitations of the latter are

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