Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In this paper we fill this gap by providing a short survey of the many tests of machine intelligence that have been proposed.

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.

John McCarthy's contributions to computer science and artificial intelligence are legendary. He invented Lisp, made substantial contributions to early work in timesharing and the theory of computation, and was one of the founders of artificial intelligence and knowledge representation. This article, written in honor of McCarthy's 80th birthday, presents a brief biography, an overview of the major themes of his research, and a discussion of several of his major papers.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space.

Horvitz noted two emerging trends at the conference and in the AI field. Second is the work in scaling AI to be more integrative. Instead of the ongoing great successes of AI researches on "wedges" of AI expertise and reasoning, there's increasing work on delivering more depth and breadth of capabilities such as sensing, learning, and reasoning. "This is very hard," notes Horvitz, "(but already) I see bits and pieces here and there." Game Playing Competition, the Poker AAI's Twenty-second Conference (AAAI-07) continued a longstanding the 1,025 attendees to choose Competition, and the Human Versus tradition of excellence.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space.

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of $10^9$ edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.

We are very pleased to republish here extended versions of a sample of the papers drawn from the Innovative Applications of Artificial Intelligence Conference (IAAI-06), which was held July 17-20, 2006, in Boston, Massachusetts. Three of these articles describe deployed applications and two describe emerging applications.

This paper is a survey of a large number of informal definitions of ``intelligence'' that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books. Nevertheless, the 70-odd definitions presented here are, to the authors' knowledge, the largest and most well referenced collection there is.

Mixed-initiative assistants are agents that interact seamlessly with humans to extend their problem-solving capabilities or provide new capabilities. Developing such agents requires the synergistic integration of many areas of AI, including knowledge representation, problem solving and planning, knowledge acquisition and learning, multiagent systems, discourse theory, and human-computer interaction. This paper introduces seven aspects of mixed-initiative reasoning (task, control, awareness, communication, personalization, architecture, and evaluation) and discusses them in the context of several state-of-the-art mixed-initiative assistants. The goal is to provide a framework for understanding and comparing existing mixed-initiative assistants and for developing general design principles and methods.