We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and learning, sketching and concept maps to improve interactivity, and a distributed agent architecture hosted on a cluster to achieve performance and longevity. We outline some results on learning by accumulating examples derived from our first experimental version.

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.

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

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.

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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.

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.

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.

We introduce a logic for reasoning about evidence that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after making the observation). We provide a sound and complete axiomatization for the logic, and consider the complexity of the decision problem. Although the reasoning in the logic is mainly propositional, we allow variables representing numbers and quantification over them. This expressive power seems necessary to capture important properties of evidence.

In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregating large corpora of internally incoherent probability assessments. The algorithm is characterized by a provable performance guarantee, and is demonstrated to be orders of magnitude faster than existing tools when tested on several real-world data-sets. In addition, unexpected connections between research in risk assessment and wireless sensor networks are exposed, as several key ideas are illustrated to be useful in both fields.