This was a large applied research effort that presented many opportunities for unusual experiences. In one such experience, I was called in, at the last minute, to help improve our ALV proposal. The proposal was a 300-page document that segued smoothly from problem description to corporate capabilities and managerial plan, omitting any mention of technical approach. This taught me a rule of thumb I have seen validated many times: the larger the project (in dollars and scope), the poorer the technical proposal. In a second experience, I was demonstrating a dynamic programming algorithm at a quarterly review.

It seemed miraculous at the time; a situated automaton that knew things without needing any models. However, I thought of it as (sensor-driven) feedback control, versus (plan driven, eyes shut) feed-forward control. I then used Mike Georgeff's procedural reasoning system (PRS) to make Flakey not only drive but navigate an office building. In some respects this project succeeded: the robot's "domain knowledge" was nothing more than a static connection graph--no distances to drive, no widths of halls or doorways, no a priori obstacles--such information was acquired en route from sensory input. In other respects, however, progress was unsatisfying.

Software personal assistants continue to be a topic of significant research interest. This article outlines some of the important lessons learned from a successfully-deployed team of personal assistant agents (Electric Elves) in an office environment. In the Electric Elves project, a team of almost a dozen personal assistant agents were continually active for seven months. Each elf (agent) represented one person and assisted in daily activities in an actual office environment. This project led to several important observations about privacy, adjustable autonomy, and social norms in office environments. In addition to outlining some of the key lessons learned we outline our continued research to address some of the concerns raised.

Our top and front-end call center software (no mean feat: these were Windows PCs simulating IBM "green screens"!), and tuning the recommender to produce high quality recommendations that were successful against historical sales data. The recommendations were designed to be delivered in real time to the call center agents during live inbound calls. For instance, if the customer ordered the pink housecoat, the recommender might suggest the fuzzy pink slippers to go with it, based on prior sales experience. The company was ready for a big test: our lead consultant was standing behind one of the call center agents, watching her receive calls. Then the moment came: the IT folk at the company pushed the metaphoric big red button and switched her over to the automated recommender system.

On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on “Whatever Happened to AI?” at the Stanford Spring Symposium presentation – to a lively audience of active AI researchers and formerly-active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, etc.)  This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing Test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI.

In response, AI the poster's size, artistic constraints, Magazine has developed a poster to and diversity of perspectives, not all help educate students, faculty, and the suggestions could be included in the public about AI and to spur them to final design, but all were greatly appreciated. I also thank AAAI, the National The poster's design was based on Science Foundation, Microsoft input from experts on how to convey Research, and Yahoo!Research for their key aspects of AI and to capture the generous support. The are included at the poster web design does not attempt the impossible site, www.aaai.org/AILandscape.php. Nor does it present a list of new support of the poster project, especially advances, which would soon become Mike Hamilton, whose many contributions obsolete. Instead, it presents a snapshot played a key role throughout. of a few aspects of AI selected to catalyze interest and to prompt viewers The poster was designed by James to find out more by exploring AAAI Gary, of Brooklyn, New York.

This special issue of AI Magazine is dedicated to the proposition that problems populate the path to insight, implying the experiences and lessons learned should be shared.

Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.

This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.

In this study, we introduce general frame of MAny Connected Intelligent Particles Systems (MACIPS). Connections and interconnections between particles get a complex behavior of such merely simple system (system in system).Contribution of natural computing, under information granulation theory, are the main topic of this spacious skeleton. Upon this clue, we organize different algorithms involved a few prominent intelligent computing and approximate reasoning methods such as self organizing feature map (SOM)[9], Neuro- Fuzzy Inference System[10], Rough Set Theory (RST)[11], collaborative clustering, Genetic Algorithm and Ant Colony System. Upon this, we have employed our algorithms on the several engineering systems, especially emerged systems in Civil and Mineral processing. In other process, we investigated how our algorithms can be taken as a linkage of government-society interaction, where government catches various fashions of behavior: solid (absolute) or flexible. So, transition of such society, by changing of connectivity parameters (noise) from order to disorder is inferred. Add to this, one may find an indirect mapping among finical systems and eventual market fluctuations with MACIPS. In the following sections, we will mention the main topics of the suggested proposal, briefly Details of the proposed algorithms can be found in the references.