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.

Conversational dialogue is an oft-cited example of mixed-initiative interaction, referring to the ability of each participant in a dialogue to take initiative to guide or add to a discussion. Endowing an automated dialogue system communicate, and coordinate with with the ability both to take initiative ("What In the course like to add a side trip.") However, of efforts to achieve goals while immersed mixed-initiative interaction extends beyond in shared context. We continue to engage spoken conversations to include a broad spectrum one another in efficient, tightly woven of collaborative problem solving marked collaborations, reasoning with remarkable efficiency by an interleaving of contributions by different about the beliefs, preferences, intentions, participants. Mastering mixed-initiative interaction poses The inferences underlying successful collaborations a constellation of fascinating challenges and typically stream in such an effortless opportunities for AI researchers.

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Schultz will speak on moving toward Intelligence Conference (IAAI-Vancouver. Oren Etzioni (University of Washington of New South Wales), distinguished for and Farecast) is known for his his work in automated reasoning, will technical work in intelligent agents, speak on representing and reasoning data mining and web search. Coolest Web Sites" and PC World's "20 progress report on logic for automated Applications of AI Conference Intelligent Arts) was the technical director and agents is another research area that experienced designer for Electronic Art's FIFA franchise He is obsessed Careful thought was put into inviting College Park) is a prominent with creating the illusion of life world class speakers to this year's conference. Getoor will speak on graph application of AI. In his talk, entitled Alan Mackworth (University of British identification, specifically methods "Big'A,' Small'I': Smart Ends from Columbia). For the technical AAAI-07 that transform an observed input Simple Means," Brown will cover various conference, speakers include Alan graph into an inferred output graph.

Mixed-initiative systems are a popular approach to building intelligent systems that can collaborate naturally and effectively with people. But true collaborative behavior requires an agent to possess a number of capabilities, including reasoning, communication, planning, execution, and learning. We describe an integrated approach to the design and implementation of a collaborative problem-solving assistant based on a formal theory of joint activity and a declarative representation of tasks. This approach builds on prior work by us and by others on mixed-initiative dialogue and planning systems.

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.

The Fifteenth Annual AAAI Robot Competition and Exhibition was held at the Twenty-First National Conference on Artificial Intelligence in Boston, Massachusetts, in July 2006. This article describes the events that were held at the conference, including the Scavenger Hunt, Human Robot Interaction, and Robot Exhibition.

Summer calendar of AI events.

Will we see autonomous humanoid robots that play (and win) soccer against the human soccer world champion in the year 2050? This question is not easy to answer, and the idea is quite visionary. However, this is the goal of the RoboCup Federation. There are serious research questions that have to be tackled behind the scenes of a soccer game: perception, decision making, action selection, hardware design, materials, energy, and more. RoboCup is also about the nature of intelligence, and playing soccer acts as a performance measure of systems that contain artificial intelligence -- in much the same way chess has been used over the last century. This article outlines the current situation following 10 years of research with reference to the results of the 2006 World Championship in Bremen, Germany, and discusses future challenges.