This paper presents an approach to integrating planning and execution in time-sensitive environments. We present a simple setting in which to consider the issue, that we call continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. We take the objective to be a form of time-dependent partial satisfaction planning reminiscent of discounted MDPs: goals offer reward that decays over time, actions incur fixed costs, and the agent attempts to maximize net utility. We argue that this setting highlights the central challenge of time-aware planning while excluding the complexity of non-deterministic actions. Our approach to this problem is based on real-time heuristic search. We view the two central issues as the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. We propose an extension of Russell and Wefald's decision-theoretic A* algorithm that can cope with our inadmissible heuristic. Our algorithm, DTOCS, handles the complexities of the on-line setting by balancing deliberative planning and real-time response.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2008 Fall Symposium Series, held Friday through Sunday, November 7-9, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia were (1) Adaptive Agents in Cultural Contexts, (2) AI in Eldercare: New Solutions to Old Problems, (3) Automated Scientific Discovery, (4) Biologically Inspired Cognitive Architectures, (5) Education Informatics: Steps toward the International Internet Classroom, (6) Multimedia Information Extraction, and (7) Naturally Inspired AI.

These underpinnings in genetics and fields are vast, variegated, informed by memetics, studying phenomena such disparate theoretical and technical disciplines, as coalition formation in an artificial and interrelated. Other applications provided an updated perspective ethical concerns related to the use of included case-based retrieval of to a previous symposium held in fall eldercare technology to ensure that narratives culturally relevant to a 2005 on the same topic. Some models focused One major theme of the symposium The symposium ended with a more directly on adaptation, from machine-learning was to investigate the use of sensor brainstorming session on possible solutions and game-theoretic networks in the home environment to for two real-life scenarios for perspectives, but discussions suggested provide safety, to monitor activities of ailing elders and their caregivers. The ways in which those adaptations daily living, to assess physical and cognitive exercise was helpful in grounding the might vary from one cultural context function, and to identify participants in the lives of older adults to another. Work was also should address real needs.

Bootstrap Voting Experts (BVE) is an extension to the Voting Experts algorithm for unsupervised chunking of sequences. BVE generates a series of segmentations, each of which incorporates knowledge gained from the previous segmentation. We show that this method of bootstrapping improves the performance of Voting Experts in a variety of unsupervised word segmentation scenarios, and generally improves both precision and recall of the algorithm. We also show that Minimum Description Length (MDL) can be used to choose nearly optimal parameters for Voting Experts in an unsupervised manner.