ACT grew out of and semantic nets. It differs from other cognitive research on human memory. Over the years, architectures in that it also includes signals, ACT has evolved into ACT* and more recently, images, and maps in its knowledge database, ACT-R. ACT-R is being used in several research and maintains a tight real-time coupling projects in an Advanced Decision Architectures between iconic and symbolic data structures in Collaborative Technology Alliance for the U.S. its world model. The 4D/RCS architecture is also Army (Gonzalez 2003). ACT-R is also being different in its (1) focus on task decomposition used by thousands of schools across the country as the fundamental organizing principle; as an algebra tutor--an instructional system (2) level of specificity in the assignment of duties that supports learning by doing. Another wellknown and responsibilities to agents and units in and widely used architecture is Soar the behavior-generating hierarchy; and (3) emphasis (Laird, Newell, and Rosenbloom 1987). Soar on controlling real machines in realworld grew out of research on human problem solving environments.

Calendar of AI-related conferences, meetings, and symposia for 2006.

A recurring requirement for human-level artificial intelligence is the incorporation of vast amounts of knowledge into a software agent that can use the knowledge in an efficient and organized fashion. This article discusses representations and processes for agents and behavior models that integrate large, diverse knowledge stores, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We introduce a new approach to comparing frameworks based on the notions of commitment, reconsideration, and a categorization of representations and processes. We review four agent frameworks, concentrating on the major representations and processes each directly supports. By organizing the approaches according to a common nomenclature, the analysis highlights points of similarity and difference and suggests directions for integrating and unifying disparate approaches and for incorporating research results from one framework into alternatives.

The seeds of AI were sewn at the Dartmouth Conference in the summer of 1956. John McCarthy, then an assistant mathematics professor at Dartmouth, organized the conference and coined the name "artificial intelligence" in his conference proposal. This summer AAAI celebrates the first 50 years of AI; and continues to foster the fertile fields of AI at the National AI conference (AAAI-06) and Innovative Applications of AI conference (IAAI-06) in Boston.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. I also consider the high-level programming language that embodies these commitments, drawing examples from the domain of in-city driving. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.

Making progress toward human-level artificial intelligence often seems to require a large number of difficult-to-integrate computational methods and enormous amounts of knowledge about the world. This article provides evidence from linguistics, cognitive psychology, and neuroscience for the cognitive substrate hypothesis that a relatively small set of properly integrated data structures and algorithms can underlie the whole range of cognition required for human-level intelligence. Some computational principles (embodied in the Polyscheme cognitive architecture) are proposed to solve the integration problems involved in implementing such a substrate. A natural language syntactic parser that uses only the mechanisms of an infant physical reasoning model developed in Polyscheme demonstrates that a single cognitive substrate can underlie intelligent systems in superficially very dissimilar domains. This work suggests that identifying and implementing a cognitive substrate will accelerate progress toward human-level artificial intelligence.

This article describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality-based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in that we have joined a number of component technologies that have not been integrated before. This integration has not only raised new research issues, but it has also suggested some new approaches to difficult problems. We describe the key capabilities of the virtual humans, including task representation and reasoning, natural language dialogue, and emotion reasoning, and show how these capabilities are integrated to provide more human-level intelligence than would otherwise be possible.

Scientists Look at Promise, Peril of Technology. News" collection that can be found--complete'There's a whole generation California and the journal'Science' convened a Odyssey" and wanted to do artificial intelligence. Ten years behind them are "Star Meets Imagination' set a record, attracting Brown, the former chief scientist for the Xerox'You've got to ask, do we "Anyone what these issues are really going to mean to'If we That's the philosophy behind the new £1 faces the same challenge -- how to don't have the right kind of scientific literacy, million Connect science and technology make a story interesting and accessible, all scientific debate becomes ideological.' 'Movies don't have to artificial intelligence.... He says technology, ... The exhibition space has been divided be accurate,' he says.

Vast information from the neurosciences may enable bottom-up understanding of human intelligence; that is, derivation of function from mechanism. This article describes such a research program: simulation and analysis of the circuits of the brain has led to derivation of a detailed set of elemental and composed operations emerging from individual and combined circuits. The specific hypothesis is forwarded that these operations constitute the "instruction set" of the brain, that is, the basic mental operations from which all complex behavioral and cognitive abilities are constructed, establishing a unified formalism for description of human faculties ranging from perception and learning to reasoning and language, and representing a novel and potentially fruitful research path for the construction of human- level intelligence.

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