The 1993-95 period presented various "identity challenges" to the field of AI and to AAAI as a leading scientific society for the field. The euphoric days of the mid-1980s AI boom were over, various expectations of those times had not been met, and there was continuing concern about an AI "winter." The major challenge of these years was to chart a path for AI, designed and endorsed by the broadest spectrum of AI researchers, that built on past progress, explained AI's capacity for addressing fundamentally important intellectual problems and realistically predicted its potential to contribute to technological challenges of the coming decade. This reflection piece considers these challenges and the ways in which AAAI helped the field to move forward. Adolescence, the twenties, and the forties each bring particular "developmental" challenges to people, and, though surely coincidentally, elements of those life stages seem also to characterize the period of my presidency.

The long-term goal of AI is human-level AI. This is still not directly definable, although we still know of human abilities that even the the best present programs on the fastest computers have not been able to emulate, such as playing master-level go and learning science from the Internet. Basic researchers in AI should measure their work as to the extent to which it advances this goal.

AAAI has seen great ups and downs, based largely on the perceived success of AI in business applications. Great early success allowed AAAI to weather the "AI winter" to enjoy the current "thaw." Other challenges to AAAI have resulted from its success in spinning out international conferences, thereby effectively removing several key AI areas from the AAAI National Conference. AAAI leadership continues to look for ways to deal with these challenges. AAI began life intending to be completely societies (such as ACM).

I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.

This article reprints a section of the January 1980 "Chairman's Message" of the SIGART Newsletter (No. 69). SIGART is the Special Interest Group on Artificial Intelligence, of the Association for Computing Machinery. At the time of AAAI's formation, SIGART, with its 3,800 members, was the principal AI organization in the United States, and its primary activity was publishing the "Newsletter.

The most important challenge facing AI today is enabling components to interact in larger scale systems, where modules built with multiple alternative methodologies can be incorporated into robust applications.

(ESWC 2006). (IIS 2006).

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.

In this brief history, the beginnings of artificial intelligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field.

Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods, in many commonly used benchmark examples, calls for an understanding of what classes of domains these methods are well suited for. In the investigation at hand, we derive a formal background to such an understanding. We perform a case study covering a range of 30 commonly used STRIPS and ADL benchmark domains, including all examples used in the first four international planning competitions. We *prove* connections between domain structure and local search topology -- heuristic cost surface properties -- under an idealized version of the heuristic functions used in modern planners. The idealized heuristic function is called h^+, and differs from the practically used functions in that it returns the length of an *optimal* relaxed plan, which is NP-hard to compute. We identify several key characteristics of the topology under h^+, concerning the existence/non-existence of unrecognized dead ends, as well as the existence/non-existence of constant upper bounds on the difficulty of escaping local minima and benches. These distinctions divide the (set of all) planning domains into a taxonomy of classes of varying h^+ topology. As it turns out, many of the 30 investigated domains lie in classes with a relatively easy topology. Most particularly, 12 of the domains lie in classes where FF's search algorithm, provided with h^+, is a polynomial solving mechanism. We also present results relating h^+ to its approximation as implemented in FF. The behavior regarding dead ends is provably the same. We summarize the results of an empirical investigation showing that, in many domains, the topological qualities of h^+ are largely inherited by the approximation. The overall investigation gives a rare example of a successful analysis of the connections between typical-case problem structure, and search performance. The theoretical investigation also gives hints on how the topological phenomena might be automatically recognizable by domain analysis techniques. We outline some preliminary steps we made into that direction.