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).

Just as email traffic has increased over the years since its in ception, so has the proportion that is unsolicited; some estimations have plac ed the proportion as high as 60%, and the average cost of this to business at arou nd $2000 per year, per employee (see [29] for a range of numbers and statis tics on spam). Unsolicited emails - commonly know as spam - have thereby become a daily feature of every email user's inbox; and regardless of advan ces in email filtering, spam continues to be a problem in a similar way to comp uter viruses which constantly reemerge in new guises. This leaves the res earch community with the task of continually investigating new approac hes to sorting the welcome emails (known as ham) from the unwelcome spam. W e present just such an approach to email classification and fi ltering based on a well studied data structure, the suffix tree (see [1 6] for a brief introduction). The approach is similar to many existing one s, in that it uses training examples to construct a model or profile of the class and its features, then uses this to make decisions as to the class of new example s; but it differs in the depth and extent of the anaysis. For a good overview of a number of text classification methods, see [26, 1, 31]. Using a suffix tree, we are able to compare not only single word s, as in most current approaches, but substrings of an arbitrary len gth.

Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective "swarm" intelligence. Termite colonies - for instance - build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally.

This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m^4), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.

The evaluation of incomplete satisfiability solvers depends critically on the availability of hard satisfiable instances. A plausible source of such instances consists of random k-SAT formulas whose clauses are chosen uniformly from among all clauses satisfying some randomly chosen truth assignment A. Unfortunately, instances generated in this manner tend to be relatively easy and can be solved efficiently by practical heuristics. Roughly speaking, for a number of different algorithms, A acts as a stronger and stronger attractor as the formula's density increases. Motivated by recent results on the geometry of the space of satisfying truth assignments of random k-SAT and NAE-k-SAT formulas, we introduce a simple twist on this basic model, which appears to dramatically increase its hardness. Namely, in addition to forbidding the clauses violated by the hidden assignment A, we also forbid the clauses violated by its complement, so that both A and compliment of A are satisfying. It appears that under this "symmetrization" the effects of the two attractors largely cancel out, making it much harder for algorithms to find any truth assignment. We give theoretical and experimental evidence supporting this assertion.

A non-binary Constraint Satisfaction Problem (CSP) can be solved directly using extended versions of binary techniques. Alternatively, the non-binary problem can be translated into an equivalent binary one. In this case, it is generally accepted that the translated problem can be solved by applying well-established techniques for binary CSPs. In this paper we evaluate the applicability of the latter approach. We demonstrate that the use of standard techniques for binary CSPs in the encodings of non-binary problems is problematic and results in models that are very rarely competitive with the non-binary representation. To overcome this, we propose specialized arc consistency and search algorithms for binary encodings, and we evaluate them theoretically and empirically. We consider three binary representations; the hidden variable encoding, the dual encoding, and the double encoding. Theoretical and empirical results show that, for certain classes of non-binary constraints, binary encodings are a competitive option, and in many cases, a better one than the non-binary representation.

Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.

We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper.

This document summarizes the major events at the AAAI-05 and IAAI-05 conferences, held in Pittsburgh, Pennsylvania in July 2005.

In recent years, we have witnessed the success of autonomous agents applying machine-learning techniques across a wide range of applications. However, agents applying the same machine-learning techniques in online applications have not been so successful. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged. To a large extent, the reinforcement learning methods developed to aid agents in learning have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them somewhat ineffective in online applications. In this article, we postulate that a small amount of prior knowledge and human-provided input can dramatically speed up online learning. We demonstrate that our agent HumanE -- with its prior knowledge or "experiences" about the real estate domain -- can effectively assist users in identifying requirements, especially unstated ones, quickly and unobtrusively.