Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language Kc, which extends the declarative planning language K by action costs. Kc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing ``shortest'' plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language Kc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved.

We address the problem of propositional logic-based abduction, i.e., the problem of searching for a best explanation for a given propositional observation according to a given propositional knowledge base. We give a general algorithm, based on the notion of projection; then we study restrictions over the representations of the knowledge base and of the query, and find new polynomial classes of abduction problems. We also show that our algorithm unifies several previous results.

Many problems in robust control and motion planning can be reduced to either find a sound approximation of the solution space determined by a set of nonlinear inequalities, or to the ``guaranteed tuning problem'' as defined by Jaulin and Walter, which amounts to finding a value for some tuning parameter such that a set of inequalities be verified for all the possible values of some perturbation vector. A classical approach to solve these problems, which satisfies the strong soundness requirement, involves some quantifier elimination procedure such as Collins' Cylindrical Algebraic Decomposition symbolic method. Sound numerical methods using interval arithmetic and local consistency enforcement to prune the search space are presented in this paper as much faster alternatives for both soundly solving systems of nonlinear inequalities, and addressing the guaranteed tuning problem whenever the perturbation vector has dimension one. The use of these methods in camera control is investigated, and experiments with the prototype of a declarative modeller to express camera motion using a cinematic language are reported and commented.

Successes and Challenges" by Alon Halevy, University of Washington; "Constraint Satisfaction, Santa Cruz; "Self-Reconfiguring Robots: Challenges and Successes" by Daniela Rus, Dartmouth University; "Automated Verification Graphs, Automata, and Logic" by Moshe Vardi, "Quantum Information: Fundamentals the world have gathered each summer One hundred the conference is always after conference hours. Also among this year's invited As part of the American Association As In addition to the strong technical to advance the science and practice part of a special track on AI and the track of the conference, the Fifteenth of AI, the organization continues web, Henzinger will discuss the future Innovative Applications of AI conference to play a leading role in organizing of search engines on the internet, (IAAI-03) will be collocated with and sponsoring these annual summer describing work under way to IJCAI. Through the years, we have conferences. Other invited talks include "Deploying in a broad range of computer systems, Information Agents on the machinery, and electronic devices Because AI is an umbrella term for a of Southern California; "Web However, where is the newest, most This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read.

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.

Interestingly enough, even the poster session was very successful. Her presentation attracted many people in all areas of AI. "How not to Give a Talk," given and approaches but also to their to an international event with a by Eugene Freuder, from 4C, Cork, future scientific career. It gave Ph.D. students (and The format of the conference included from ENIT, Tarbes, France, and Paolo others...) crucial keys into the usual paper presentations in thematic Liberatore, from University La Sapienza, errors and blunders one can make sessions, a poster session, two invited Rome, Italy, were the cochairs of when presenting research work. STAIRS and ECAI program committees, chaired the Organization Committee. Another goal of From 64 submissions, the Program robotics at Carnegie Mellon University, STAIRS was indeed to offer researchers Committee selected 21 papers and 9 appeared to be the best candidate who have recently completed their posters of high technical quality, for such a purpose.

All accepted papers will appear in the conference proceedings published by AAAI Press. Selected authors will be invited to submit extended versions of their Ingrid Russell, University of Hartford papers to a special issue of the International Journal on Artificial Intelligence Tools irussell@hartford.edu The papers Valerie Barr, Hofstra University should not exceed 5 pages and is due by October 24, 2003. All submissions will be done Zdravko Markov, Central Connecticut State electronically via FLAIRS web submission system, which will be available through University the conference website. Please consult the conference web page for details on paper submission.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

Competitions were held at Since the first competition in 1997 (Kitano Fukuoka Dome Baseball Stadium from 19 to 23 1998), RoboCup has grown into an international June followed by the International RoboCup joint research project in which about Symposium on 24 to 25 June. It is one of RoboCup is an attempt to foster intelligent the most ambitious projects of the twenty-first robotics research by providing a standard century. RoboCup currently consists of three problem, the ultimate goal of which is to divisions: (1) RoboCupSoccer, a move toward build a team of 11 humanoid robots that the final goal; (2) RoboCupRescue, a serious social can beat the human World Cup champion application of rescue activities for any kind soccer team by 2050. It's obvious that of disaster; and (3) RoboCupJunior, an international building a robot to play a soccer game is an education-based initiative designed to immense challenge; readers might therefore introduce young students to robotics. It is our intention to use since 1997 and showed its epoch-making new RoboCup as a vehicle to promote robotics standard for future RoboCups. One thousand and AI research by offering a publicly appealing four team members from 188 teams from 30 but formidable challenge (Asada et nations around the world participated. It included al. 1999; Kitano et al. 1997). The humanoid league is a big challenge knowledge, this was the largest robotic event with a long-term, high-impact goal, which in history.

Hence, no training phase is needed. The local statistics define an with white lines; goals are blue and yellow; and expectation of "how the two sides of the curve robots are black with light blue or magenta might look." Second, refine the estimation of model parameters These stringent rules allow for simple mechanisms by (1) updating the mean of the estimation for object detection and recognition: in a maximum a posteriori step such that Segment the captured image into blobs of the the vicinity of the curve matches the expectation same color and interpret these blobs. To the defined by the local statistics and (2) updating best of our knowledge, all autonomous robot the covariance of the estimation based on soccer teams with vision-based perception apply the Hessian of the resulting objective function. However, because The two steps are repeated until there is no the RoboCup committee is planning to significant change in the estimated Gaussian make the rules more realistic, these objectrecognition distribution.