Using displays of line orientations taken from Wolfe's experiments [1992], we study the hypothesis that the distinction between parallel versus serial processes arises from the availability of global information in the internal representations of the visual scene. The model operates in two phases. First, the visual displays are compressed via principal-component-analysis. Second, the compressed data is processed by a target detector module inorder to identify the existence of a target in the display. Our main finding is that targets in displays which were found experimentally tobe processed in parallel can be detected by the system, while targets in experimentally-serial displays cannot. This fundamental difference is explained via variance analysis of the compressed representations, providing a numerical criterion distinguishing parallelfrom serial displays. Our model yields a mapping of response-time slopes that is similar to Duncan and Humphreys's "search surface" [1989], providing an explicit formulation of their intuitive notion of feature similarity. It presents a neural realization ofthe processing that may underlie the classical metaphorical explanations of visual search.

The National Aeronautics and Space Administration Jet Propulsion Laboratory (Pasadena, Calif.) has developed The chip, which has The National Aeronautics and Chester, N.Y.) to improve its ability to been licensed by automaker Ford Space Administration (NASA) Goddard match reported wage information. Motor (Dearborn, Mich.), is designed Space Flight Center (Greenbelt, The solution will help the agency to augment current vehicle on-board Md.) has developed the The Philippines (Quezon City, The process for outside scientists wanting RoyScot Trust, the asset finance arm Philippines) has adopted an intelligent to use NASA's space telescopes. of the Royal Bank of Scotland (Edinburgh, agent-based software system to The system is designed to capture and Scotland), has implemented an manage mission-critical tax processes maintain key scientific knowledge expert system-based solution to automate across The Philippines. The intelligent while it reduces common errors made the credit-underwriting process. The firm has set up a credit control management of the bureau's entire Johnson Controls (Milwaukee, Wis.), system, The turnkey expert installs and maintains. By integrating component has deployed a speech-recognition- frequent air travelers through U.S. math data with work-cell visualization based application for its frequent flier Immigration inspection in less than software, engineers can simulate customers.

A class of interval-based temporal languages for uniformly representing and reasoning about actions and plans is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal languages are members of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The subsumption problem for a class of temporal Description Logics is investigated and sound and complete decision procedures are given. The basic language TL-F is considered first: it is the composition of a temporal logic TL -- able to express interval temporal networks -- together with the non-temporal logic F -- a Feature Description Logic. It is proven that subsumption in this language is an NP-complete problem. Then it is shown how to reason with the more expressive languages TLU-FU and TL-ALCF. The former adds disjunction both at the temporal and non-temporal sides of the language, the latter extends the non-temporal side with set-valued features (i.e., roles) and a propositionally complete language.

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.

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.

Robot soccer competition provides an excellent opportunity for integrated robotics research. In particular, robot players in a soccer game must recognize and track objects in real time, navigate in a dynamic field, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond an individual's capabilities), and intelligent (reasoning and planning actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system, which raises a set of challenges that are new to individual research disciplines. This article describes our experience (problems and solutions) in these aspects. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goalkeeper, defender, or forward) and use different strategies in their behavior. Our philosophy in building these robots is to use the least sophistication to make them as robust and integrated as possible. At RoboCup-97, held as part of the Fifteenth International Joint Conference on Artificial Intelligence, these integrated robots performed well, and our DREAMTEAM won the world championship in the middle-size robot league.

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.

'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

In this article, we describe the milestone of our research efforts in our work for the RoboCup middle-size league competition. Reinforcement learning in applying it to real robot applications; we then give our method of coping with these has recently been receiving increased attention issues in the context of RoboCup. Finally, we as a method for robot learning with little or no show our system and the experimental results a priori knowledge and a higher capability for of RoboCup-97. The robot senses the current state First, we follow the explanation of Q-learning of the environment and selects an action. For a more thorough treatment, Based on the state and the action, the environment see Watkins and Dayan (1992).

We applied mobile computing to community support and explored mobile computing with a large number of terminals. This article reports on the Second International Conference on Multiagent Systems (ICMAS'96) Mobile Assistant Project that was conducted at an actual international conference for multiagent systems using 100 personal digital assistants (PDAs) and cellular telephones. We supported three types of service: (1) communication services such as e-mail and net news; (2) information services such as conference, personal, and tourist information; and (3) community support services such as forum and meeting arrangements. After the conference, we analyzed a large amount of log data and obtained the following results: It appears that people continuously used PDAs in their hotel rooms after dinner; e-mail services were used independently of the conference structure, but the load on information services reflected the schedule of the conference. Postquestionnaire data showed that our trial was considered interesting, although people were not fully satisfied with the PDAs and services provided. Participants showed a deep interest in mobile computing for community support.