In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4.

Articles in this issue describe recent approaches for integrating algorithms and data structures from diverse subfields of AI. The new applications and significant improvements to existing applications this work has enabled demonstrates the ability of integrated systems and research to continue progress towards human-level artificial intelligence.

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.

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. 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.

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. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.

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 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.

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.

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.

The Eighteenth International Conference of the Florida Artificial Intelligence Research Society was held May 15-17, 2005, at the Hilton Clearwater Beach Resort in Clearwater Beach, Florida, located on 10 acres of powder-white beaches on the Gulf of Mexico. The general chairs were Larry Holder and Diane Cook of the University of Texas at Arlington. Ingrid Russell of the University of Hartford and Zdravko Markov of Central Connecticut State University served as program chairs. This article presents a report of the conference.

From a computer science and artificial intelligence perspective, robotics often appears as a collection of disjoint, sometimes antagonistic subfields. The lack of a coherent and unified presentation of the field negatively affects teaching, especially to undergraduates. This article presents an alternative synthesis of the various subfields of AI robotics and shows how these traditional subfields fit into the whole. Finally, it presents a curriculum based on these ideas.