The model, an early selection filter guided by top-down attentional control, entertains each candidate output class in sequence and adjusts attentional gain coefficients in order to produce a strong response for that class.

Spatial information comes in two forms: direct spatial information (for example, retinal position) and indirect temporal contiguity information, since objects encountered sequentially are in general spatially close.

In this work, we formulate a hierarchical systemof interconnected modules consisting in populations of neurons formodeling the underlying mechanisms involved in selective visual attention. We demonstrate that our neural system for visual search works across the visual field in parallel but due to the different intrinsic dynamics can show the two experimentally observed modes of visual attention, namely: the serial and the parallel search mode. In other words, neither explicit model of a focus of attention nor saliencies maps are used. The focus of attention appears as an emergent property of the dynamic behavior of the system. The neural population dynamics are handled in the framework of the mean-field approximation. Consequently, thewhole process can be expressed as a system of coupled differential equations.

Virtual reality (VR) provides immersive and controllable experimental environments.It expands the bounds of possible evoked potential (EP) experiments by providing complex, dynamic environments in order tostudy cognition without sacrificing environmental control. VR also serves as a safe dynamic testbed for brain-computer .interface

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded agent. The computation time of AIXI^tl is of the order t x 2^l.

This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach. My central conjecture is that the probabilistic approach to robotics scales better to complex real-world applications than approaches that ignore a robot's uncertainty.

Language, vision, and music: What common cognitive patterns underlie our competence in these disparate modes of thought? Language (natural and formal), vision, and music seem to share at best the following attributes: a hierarchical organization of constituents, recursivity, metaphor, the possibility of self-reference, ambiguity, and systematicity. Can we propose the existence of a general symbol system with instantiations in these three modes, or is the only commonality to be found at the level of such entities as cerebral columnar automata?

The stand-allocation system (SAS) is an AI application developed for the Hong Kong International Airport (HKIA) at Chek Lap Kok. The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. SAS is an example of how development methodologies used to construct modern AI applications have become fully inline with mainstream practices.

The Innovative Applications of Artificial Intelligence Conference was held 18-22 July 1999 in Orlando, Florida. Ramasamy Uthurusamy was the Program Chair and Barbara Hayes-Roth was the Program Co-Chair. Although all the IAAI-99 papers and talks were certainly interesting and important, we present in this special issue of AI Magazine only a select subset because of page and other limitations. We include two invited talks and four applications as a snapshot of IAAI-99.

In this article, we present a last-minute travel application as part of a complete virtual travel agency. Each year, a significant amount of tour packages are sold as last minute tours in Germany. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. Important for the acceptance by customers is also the integration into the virtual travel agency and the interconnections to other parts of this system, such as background information or the online car rental application.