Ali Erdem

Interactive explanation of software systems

This paper describes an effort to provide automated support for the interactive inquiry and explanation process that is at the heart of software understanding. A hypermedia tool called I-Doc allows software engineers to post queries about a software system, and generates focused explanations in response. These explanations are task oriented, i.e., they are sensitive to the software engineering task being performed by the user that led to the query. Task orientation leads to more effective explanations, and is particularly helpful for understanding large software systems. Empirical studies of inquiry episodes were conducted in order to investigate this claim: the kinds of questions users ask, their relation to the users task and level of expertise. The I-Doc tool is being developed to embody these principles, employing knowledge-based techniques. The presentation mechanism employs World Wide Web (WWW) technology, making it suitable for widespread use.

Task oriented software understanding

The main factors that affect software understanding are the complexity of the problem solved by the program, the program text, the users mental ability and experience and the task being performed. The paper describes a planning approach solution to the software understanding problem that focuses on the users task and expertise. First, user questions about software artifacts have been studied and the most commonly asked questions are identified. These questions are organized into a question model and procedures for answering them are developed. Then, the patterns in user questions while performing certain tasks have been studied and these patterns are used to build generic task models. The explanation system uses these task models in several ways. The task model, along with a user model, is used to generate explanations tailored to the users task and expertise. In addition, the task model allows the system to provide explicit task support in its interface.

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.

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.

Using an Explicit Teamwork Model and Learning in RoboCup: An Extended Abstract

The RoboCup research initiative has established synthetic and robotic soccer as testbeds for pursuing research challenges in Articial Intelligence and robotics. This extended abstract focuses on teamwork and learning, two of the multi- agent research challenges highlighted in RoboCup. To address the challenge of teamwork, we discuss the use of a domain-independent explicit model of team- work, and an explicit representation of team plans and goals. We also discuss the application of agent learning in RoboCup.

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.

ISIS: An Explicit Model of Teamwork at RobotCup-97

56 AI MAGAZINE Group A: LAI (Université Carlos III De Madrid), FC MELLON (CMU), RM KNIGHTS (RMIT), ICHIMURA (Kinki University, Japan). Group B: RIEKKI (University of Oulu, Finland), CMUNITED (CMU), HEADLESS CHICKENS (RMIT), NIT-STONES (Nagoya Institute of Technology, Japan). Group C: MICROB (Université de Paris VI), BALCH (Georgia Institute of Technology), PROJECT MAGI (Aoyama University, Japan), OHTA (Tokyo Institute of Technology, Japan). Group D: AT HUMBOLDT (Humboldt University, Germany), TEAM SICILY (Stanford University), KASUGA-BITO (Chubu University, Japan), ANDHILL (Tokyo Institute of Technology, Japan). Group E: PAGELLO (University of Padua, Italy), HAARLEM (Chukyo University, Japan), ORIENT (Toyo University, Japan). Group F: UBC DYNAMO (University of British Columbia, Canada), LUKE (University of Maryland), OGALETS (University of Tokyo, Japan), TUT (Toyohashi University of Technology, Japan). Group G: CHRISTENSEN (Charlmers University of Technology, Sweden), TEAM GAMMA (ETL, Japan), KOSUE (Kinki University, Japan). Group H: ISIS (USC-ISI), GARBAGE COLLECTORS (private, Japan), I&W (Waseda University, Japan).