This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.

Modeling various aspects of language-syntax, semantics, pragmatics, and discourse, among others -- by the use of constrained formal-computational systems, just adequate for such modeling, has proved to be an effective research strategy, leading to deep understanding of these aspects, with implications for both machine processing and human processing. This approach enables one to distinguish between the universal and stipulative constraints. This is in contrast to an approach where we start with the most powerful formal-computational system and then model the phenomena by making all constraints stipulative in a sense. The use of constrained systems for modeling leads to some novel ways of describing locality of structures and brings out the relationship between the complexity of description of primitives and local computations over them. These ideas serve to unify theoretical, computational, and statistical aspects of natural language processing in AI. It is expected that this approach will also be productive in other domains of AI.

Robotic soccer is a challenging research domain that involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this article, we describe CMUNITED, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and implemented algorithms for strategic collaboration between the robots in an uncertain and dynamic environment. The robots can organize themselves in formations, hold specific roles, and pursue their goals. In game situations, they have demonstrated their collaborative behaviors on multiple occasions. We present an overview of the vision-processing algorithm that successfully tracks multiple moving objects and predicts trajectories. The article then focuses on the agent behaviors, ranging from low-level individual behaviors to coordinated, strategic team behaviors. CMUNITED won the RoboCup-97 small-robot competition at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan.

RoboCup-97, The First Robot World Cup Soccer Games and Conferences, was held at the Fifteenth International Joint Conference on Artificial Intelligence. There were two leagues: (1) real robot and (2) simulation. Ten teams participated in the real-robot league and 29 teams in the simulation league. Over 150 researchers attended the technical workshop. The world champions are CMUNITED (Carnegie Mellon University) for the small-size league, DREAMTEAM (University of Southern California) and TRACKIES (Osaka University, Japan) for the middle-size league, and AT-HUMBOLDT (Humboldt University) for the simulation league. The Scientific Challenge Award was given to Sean Luke (University of Maryland) for his genetic programming- based simulation team LUKE, and the Engineering Challenge Awards were given to UTTORI UNITED (Utsunomiya University, Toyo University, and Riken, Japan) and RMIT (Royal Melbourne Institute of Technology, Australia) for designing novel omnidirectional driving mechanisms. Over 5000 spectators and 70 international media covered the competition worldwide. RoboCup-98, the Second Robot World Cup Soccer, was held in conjunction with the Third International Conference on Multiagent Systems in Paris, France, in July 1998.

The robot uses a layered Intelligence (AAAI-97). Twenty-one robotic architecture for integrating planning and teams participated, making this the largest action. It differs from the usual approach of robot exhibition ever. See figure 1 for a photo interfacing a planner to a reactive system in a of the exhibition participants. Since the first layered architecture because the reactive system Mobile Robot Competition and Exhibition at is replaced with a different kind of action AAAI-92, the exhibition has served to demonstrate system.

's performance in is driven by's development was driven by the Using Further aspects of multiagent agents could not always quickly locate and agent and team modeling. With respect to learning, as well as arenas of agent and intercept the ball or maintain awareness of teamwork, our previous work was based on team modeling (particularly to recognize positions of teammates and opponents. It then enables team members to make any decisions. Instead, all the decision Yaser Al-Onaizan, Ali Erdem, autonomously reason about coordination making rests with the higher level, Gal A. Kaminka, Stacy C. Marsella, and communication in teamwork, providing implemented in the Given its domain architecture, which takes into account the independence, it also enables reuse across recommendations made by the lower level. 's teamwork reasoning is currently test domain given its substantial also implemented in

Please send papers to: AAAI-99--American Association for Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025-3442 Registration and call clarification inquiries (only) may be sent to ncai@aaai.org.

This article gives an overview of two recent events on the validation and verification of knowledge-based systems: (1) the 1997 European Symposium on the Verification and Validation of Knowledge-Based Systems (EUROVAV-97) and (2) the Four-teenth National Conference on Artificial Intelligence Workshop on the Verification and Validation of Knowledge- Based Systems. To give an integrated view of current research issues in this field, we organized this article along thematic lines, unifying the reports of the two separate meetings. Our report focuses on the trends that we think will be important in the near future in this field.

This article studies the problem of modifying the action ordering of a plan in order to optimise the plan according to various criteria. One of these criteria is to make a plan less constrained and the other is to minimize its parallel execution time. Three candidate definitions are proposed for the first of these criteria, constituting a sequence of increasing optimality guarantees. Two of these are based on deordering plans, which means that ordering relations may only be removed, not added, while the third one uses reordering, where arbitrary modifications to the ordering are allowed. It is shown that only the weakest one of the three criteria is tractable to achieve, the other two being NP-hard and even difficult to approximate. Similarly, optimising the parallel execution time of a plan is studied both for deordering and reordering of plans. In the general case, both of these computations are NP-hard. However, it is shown that optimal deorderings can be computed in polynomial time for a class of planning languages based on the notions of producers, consumers and threats, which includes most of the commonly used planning languages. Computing optimal reorderings can potentially lead to even faster parallel executions, but this problem remains NP-hard and difficult to approximate even under quite severe restrictions.

A computer program that uses AI planning techniques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program -- a new version of Great Game Products' BRIDGE BARON program -- won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game tree search techniques used in computer programs for games such as Chess and Checkers work differently from how humans think about such games. In contrast, our new version of the BRIDGE BARON emulates the way in which a human might plan declarer play in Bridge by using an adaptation of hierarchical task network planning. This article gives an overview of the planning techniques that we have incorporated into the BRIDGE BARON and discusses what the program's victory signifies for research on AI planning and game playing.