Most autonomous robots are equipped with restricted, unreliable, and inaccurate sensors and effectors and operate in complex and dynamic environments. A successful approach to deal with the resulting uncertainty is the use of controllers that prescribe the robots' behavior in terms of concurrent reactive plans (CRPs) -- plans that specify how the robots are to react to sensory input in order to accomplish their jobs reliably (e.g., McDermott, 1992a; Beetz, 1999). Reactive plans are successfully used to produce situation specific behavior, to detect problems and recover from them automatically, and to recognize and exploit opportunities (Beetz et al., 2001). These kinds of behaviors are particularly important for autonomous robots that have only uncertain information about the world, act in dynamically changing environments, and are to accomplish complex tasks efficiently. Besides reliability and flexibility, foresight is another important capability of competent autonomous robots (McDermott, 1992a).

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.

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 describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data-mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application. In particular, we outline the need to bring the analysts into the process and for heuristic improvements to the plan-recognition algorithm.

We are pleased to publish this special selection of articles from the Sixteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-04), which occurred July 27-29, 2004 in San Jose, California. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The seven articles selected for this special issue are extended versions of the papers that appeared at the conference. Four of the articles describe deployed applications that are already in use in the field. The other three articles, which are from the emerging technology track, were selected because they are particularly innovative and show great potential for deployment.

This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. These techniques should be of interest to planning practitioners working on knowledge-rich application domains. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.

Today, approximately 10 percent of the world's population is over the age of 60; by 2050 this proportion will have more than doubled. Moreover, the greatest rate of increase is amongst the "oldest old," people aged 85 and over. While many older adults remain healthy and productive, overall this segment of the population is subject to physical and cognitive impairment at higher rates than younger people. This article surveys new technologies that incorporate artificial intelligence techniques to support older adults and help them cope with the changes of aging, in particular with cognitive decline.

The Graphplan algorithm for generating optimal make-span plans containing parallel sets of actions remains one of the most effective ways to generate such plans. However, despite enhancements on a range of fronts, the approach is currently dominated in terms of speed, by state space planners that employ distance-based heuristics to quickly generate serial plans. We report on a family of strategies that employ available memory to construct a search trace so as to learn from various aspects of Graphplan's iterative search episodes in order to expedite search in subsequent episodes. The planning approaches can be partitioned into two classes according to the type and extent of search experience captured in the trace. The planners using the more aggressive tracing method are able to avoid much of Graphplan's redundant search effort, while planners in the second class trade off this aspect in favor of a much higher degree of freedom than Graphplan in traversing the space of'states' generated during regression search on the planning graph. The tactic favored by the second approach, exploiting the search trace to transform the depth-first, IDA* nature of Graphplan's search into an iterative state space view, is shown to be the more powerful. We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation targeted specifically at planning graph search. Guided by such a heuristic, the step-optimal version of the planner in this class clearly dominates even a highly enhanced version of Graphplan. By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a modern heuristic state space planner.

A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making domains involve large state spaces and large, stochastic action sets. We investigate a way to act intelligently as quickly as possible in domains where finding a complete policy would take a hopelessly long time.