Sheila Tejada

The Ariadne approach to Web- based information integration

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

GameBots: a flexible test bed for multiagent team research

GameBots [1] is a virtual reality platform that allows the creation and evaluation of intelligent agents that interact with a rich 3D continuous dynamic environment. As opposed to previous test beds that focus on a single task and environment (such as soccer simulation [4]), GameBots does not define a single benchmark task. Instead, the GameBots platform comes with a wide variety of predefined tasks and environments and allows anyone to extend these in various ways, or create new challenges. This enables multiagent systems (MAS) and artificial intelligence researchers to explore a wide variety of algorithms and techniques, in areas such as spatial navigation, learning, dynamic resource allocation, multiagent planning, plan-recognition, collaboration, distributed adversarial planning, and human-machine teamwork. GameBots is composed of two components. The first of these is a freely-available open source extension of the commercial Unreal Tournament game engine [3]. It defines a socket-based API allowing anyone to create agents that can participate in any Unreal Tournament games. The second component is a set of development tools, sample source code, and nonviolent graphics (replacements for the default graphics) that form a basic development environment to help users get started in using GameBots. Gal A. Kaminka, Manuela M. Veloso, Steve Schaffer,

Ariadne: a system for constructing mediators for Internet sources

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.

Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education

This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI.

Building integrated mobile robots for soccer competition

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform real-time visual recognition, navigate in a dynamic field, track moving objects, 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 individuals capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system. To build such integrated robots, we should use different approaches from those employed in separate research disciplines. This paper describes our experience (problems and solutions) in this aspect for building soccer robots. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In RoboCup97, our Dreamteam robots performed well (scored 8 of 9 goals of all teams in the league) and won the world championship in the middle-sized robot league.

Toward Integrated Soccer Robots

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 individuals 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.

Yoda: The Young Observant Discovery Agent

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our goal is to create a mobile agent that can autonomously learn from its environment based on its own actions, percepts, and mis-sions. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODAs software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. The planner uses a map of the environment to determine a sequence of goals to be accomplished by the robot and delegates the detailed executions to the set of behaviors at the lower layer. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODAs performance at the robot competition.

Integrated Reactive Soccer Agents

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform realtime visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and hit 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 individuals capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). To build such integrated robots, we should use different approaches from those employed in separate research disciplines. In the 1997 RoboCup competition, the USC/ISI robot team, called Dreamteam, fought hard and won the world championship in the middle-sized robot league. These robots all share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goal-keeper, defender or forward) and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. This paper describes our experiences during the competition as well as our new improvements to the team.

Autonomous Soccer Robots

The Robocup 97 competition provides an excellent opportunity to demonstrate the techniques and methods of artificial intelligence, autonomous agents and computer vision. On a soccer field the core capabilities a player must have are to navigate the field, track the ball and other agents, recognize the difference between agents, collaborate with other agents, and hit the ball in the correct direction. USCs Dreamteam of robots can be described as a group of mobile autonomous agents collaborating in a rapidly changing environment. The key characteristic of this team is that each soccer robot is an autonomous agent, self-contained with all of its essential capabilities on-board. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goalkeeper, defender or forward) and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In the 1997 RoboCup competition, the Dreamteam played well and won the world championship in the middle-sized robot league.

Building integrated robots for soccer competition

Middle sized robot soccer competition provides an excellent opportunity for distributed robotic systems. In particular, a team of dog sized robot players must perform real time visual recognition, navigate in a dynamic field, track moving objects and collaborate with teammates (R.C. Arkin, 1987). Our design philosophy for the system architecture is that we view each robot as a complete and active physical entity, who can intelligently maneuver and perform in realistic and challenging surroundings. In order to survive the rapidly changing environment in a soccer game, each robot must be physically strong, computationally fast, and behaviorally accurate. Considerable importance is given to an individual robots ability to perform on its own without any off-board resources such as global, birds eye view cameras or remote computing processors. Each robots behavior must base on its own sensor data, decision making software, and eventually communication with teammates.