Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high-quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning assistant. That is, we propose to model planning as a goal-manipulation task. Here planning involves moving goals through a hyperspace in order to reach equilibrium between available resources and the constraints of a dynamic environment. The users can establish and "steer" goals through a visual representation of the planning domain. They can associate resources with particular goals and shift goals along various dimensions in response to changing conditions as well as change the structure of previous plans. Users need not know the details of the underlying technology, even when search is used within. This article empirically examines user performance under both the search and the goal-manipulation models of planning and shows that many users do better with the latter.

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.

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.

The judicious use of abstraction can help planning agents to identify key interactions between actions, and resolve them, without getting bogged down in details. However, ignoring the wrong details can lead agents into building plans that do not work, or into costly backtracking and replanning once overlooked interdependencies come to light. We claim that associating systematically-generated summary information with plans' abstract operators can ensure plan correctness, even for asynchronously-executed plans that must be coordinated across multiple agents, while still achieving valuable efficiency gains. In this paper, we formally characterize hierarchical plans whose actions have temporal extent, and describe a principled method for deriving summarized state and metric resource information for such actions. We provide sound and complete algorithms, along with heuristics, to exploit summary information during hierarchical refinement planning and plan coordination. Our analyses and experiments show that, under clearcut and reasonable conditions, using summary information can speed planning as much as doubly exponentially even for plans involving interacting subproblems.

Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.

Though attention to evaluating human-robot interfaces has increased in recent years, there are relatively few reports of using evaluation tools during the development of humanrobot interaction (HRI) systems to improve their designs. Heuristic evaluation is a technique suitable for such applications that has become popular in the humancomputer interaction (HCI) community. However, it requires usability heuristics applicable to the system environment. This work contributes a set of heuristics appropriate for use with HRI systems, derived from a variety of sources both in and out of the HRI field. Evaluators have successfully used the heuristics on an HRI system, demonstrating their utility against standard measures of heuristic effectiveness.

Advances in semiconductor technology are contributing to the increasing complexity in the design of embedded systems. Architectures with novel techniques such as evolvable nature and autonomous behavior have engrossed lot of attention. This paper demonstrates conceptually evolvable embedded systems can be characterized basing on acausal nature. It is noted that in acausal systems, future input needs to be known, here we make a mechanism such that the system predicts the future inputs and exhibits pseudo acausal nature. An embedded system that uses theoretical framework of acausality is proposed. Our method aims at a novel architecture that features the hardware evolability and autonomous behavior alongside pseudo acausality. Various aspects of this architecture are discussed in detail along with the limitations.

Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, including nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost covering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin packing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches.

The American Association for Artificial Intelligence was pleased to present the AAAI 2006 Fall Symposium Series, held Friday through Sunday, October 13-15, at the Hyatt Regency Crystal City in Washington, DC. Seven symposia were held. The titles were (1) Aurally Informed Performance: Integrating Ma- chine Listening and Auditory Presentation in Robotic Systems; (2) Capturing and Using Patterns for Evidence Detection; (3) Developmental Systems; (4) Integrating Reasoning into Everyday Applications; (5) Interaction and Emergent Phenomena in Societies of Agents; (6) Semantic Web for Collaborative Knowledge Acquisition; and (7) Spacecraft Autonomy: Using AI to Expand Human Space Exploration.

This year's program, to be held in conjunction later this spring!