For example, priming in a subliminal perception task was found to be confined to a quadrant of the visual field [16]. The notion that the representation of an object may be tied to a particular location in the visual field where it is first observed is compatible with the concept of object file, a hypothetical record created by the visual system for every encountered object, which persists as long as the object is observed. Moreover, location (as it figures in the CoF model) should be interpreted relative to the focus of attention, rather than retinotopically [17]. The idea that global relationships (hence, large-scale structure) have precedence over local ones [18], which is central to our approach, has withstood extensive testing in the past two decades. Even with the perceptual salience of the global and local structure equated, subjects are able to process the relations among elements before the elements themselves are identified [19]. More generally, humans are limited in their ability to represent spatial structure, in that the representation of spatial relations requires spatial attention. For example, visual search is difficult when above below 0. 9

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester.

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In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation. I discuss the design and properties of several agent-centered search methods, focusing on robot exploration and localization.

The Mind Doesn't Work That Way: Fodor dubs the synthesis of computationalism, we've got; indeed, the only one like the wrong paradigm for studying However, doesn't work for abductive inferences" types is innate), massive modularity we will have to add something radically (p. Fodor doesn't that the frame problem is why the part in a knowledge base antecedently think we were created, of course; instead of the human mind responsible for deemed to be irrelevant to the inference. Fodor defines irrelevant information, globality rather than by gradual, small transitions, the frame problem as the problem of (pp. Consider just one case the latter being the hallmark of "[h]ow to make abductive inferences from research on analogy: Who would classical adaptationism). This of the atom, but it was relevant.

The field of adaptive robotics involves simulations and real-world implementations of robots that adapt to their environments. In this article, I introduce adaptive environmentics -- the flip side of adaptive robotics -- in which the environment adapts to the robot. To illustrate the approach, I offer three simple experiments in which a genetic algorithm is used to shape an environment for a simulated khepera robot. I then discuss at length the potential of adaptive environmentics, also delineating several possible avenues of future research.

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They express both thoughts and California (USC)/Information Sciences Institute emotions; emotional expression is important to (ISI) is to develop new technologies that portray characteristics of enthusiasm and empathy promote effective learning and increase learner that are important for human teachers. These technologies are intended They are knowledgeable about the subject matter to result in interactive learning materials that being learned, of pedagogical strategies, and support the learning process and that complement also have knowledge about how to find and and enhance existing technologies relevant obtain relevant knowledge from available to learning such as the World Wide Web. Our work draws significant inspiration from Figure 1 shows one of the guidebots that we human learning and teaching. We piece of equipment called a high-pressure air seek a better understanding of the characteristics compressor aboard United States Navy ships. As learners view instructional materials, guidebots can provide useful commentary on these materials.

How do we decide how to represent an intelligent system in its interface, and how do we decide how the interface represents information about the world and about its own workings to a user? This article addresses these questions by examining the interaction between representation and intelligence in user interfaces. The rubric representation covers at least three topics in this context: (1) how a computational system is represented in its user interface, (2) how the interface conveys its representations of information and the world to human users, and (3) how the system's internal representation affects the human user's interaction with the system. I argue that each of these kinds of representation (of the system, information and the world, the interaction) is key to how users make the kind of attributions of intelligence that facilitate their interactions with intelligent systems. In this vein, it makes sense to represent a systmem as a human in those cases where social collaborative behavior is key and for the system to represent its knowledge to humans in multiple ways on multiple modalities. I demonstrate these claims by discussing issues of representation and intelligence in an embodied conversational agent -- an interface in which the system is represented as a person, information is conveyed to human users by multiple modalities such as voice and hand gestures, and the internal representation is modality independent and both propositional and nonpropositional.