14680149
Oracular Partially Observable Markov Decision Processes: A Very Special Case
We introduce the oracular partially observable Markov decision process (OPOMDP), a type of POMDP in which the world produces no observations; instead there is an "oracle," available in any state, that tells the agent its exact state for a fixed cost. The oracle may be a human or a highly accurate sensor. At each timestep the agent must choose whether to take a domain-level action or consult the oracle. This formulation comprises a factorization between information-gathering actions and domain-level actions, allowing us to characterize the value of information and to examine the problem of planning under uncertainty from a novel perspective. We propose an algorithm to capitalize on this factorization and the special structure of the OPOMDP, and we test the algorithm's performance on a new sample domain. On this new domain, we are able to solve a problem with hundreds of thousands of action-states and vastly outperform a previous state-of-the-art approximate technique
14322463
Automatic Clustering of Faces in Meetings
Meetings are an integral part of business life for any organization. In previous work, we have developed a physical awareness system called CAMEO (camera assisted meeting event observer) to record and process the audio/visual information of a meeting. An important task in meeting understanding is to know who and how many people are attending the meeting. In this paper, we present an automatic approach to detect, track, and cluster people's faces in long video sequences. This is a challenging problem due to the appearance variability of people's faces (illumination, expression, pose,...). Two main novelties are presented: a robust real-time adaptive subspace face tracker which combines color and appearance. A temporal subspace clustering algorithm. The effectiveness and robustness of the proposed system is demonstrated over a data set of long videos (i.e. 1 hour).
6309060
Turning Segways into Robust Human-Scale Dynamically Balanced Soccer Robots
The Segway Human Transport (HT) is a one person dynamically self-balancing transportation vehicle. The Segway Robot Mobility Platform (RMP) is a modification of the HT capable of being commanded by a computer for autonomous operation. With these platforms, we propose a new domain for human-robot coordination through a competitive game: Segway Soccer. The players include robots (RMPs) and humans (riding HTs). The rules of the game are a combination of soccer and Ultimate Frisbee rules. In this paper, we provide three contributions. First, we describe our proposed Segway Soccer domain. Second, we examine the capabilities and limitations of the Segway and the mechanical systems necessary to create a robot Segway Soccer Player. Third, we provide a detailed analysis of several ball manipulation/kicking systems and the implementation results of the CM-RMP pneumatic ball manipulation system.
195790
A Formalization of Equilibria for Multiagent Planning
Planning has traditionally focused on single agent systems. Although planning domain languages have been extended to multiagent domains, solution concepts have not. Previous solution concepts either focus on planning for teams of agents with a single goal, or on a single agent in an environment containing other agents with unknown and unpredictable behavior. In reality other agents are usually acting to achieve their own explicit goals, which may not be the same or even related. In game theory the notion of an equilibria provides a framework for thinking about self-interested, utility maximizing agents. We define a formalization of the multiagent nondeterministic planning problem and introduce a notion of equilibria inspired by the game theoretic concept. As far as we know, this is the first solution framework that explicitly accounts for the various goals of all agents. In addition to the formalization we also demonstrate how this framework applies in a number of different domains.
431152
Progress in RoboCup Soccer Research in 2000
In addition to researchers in AI and robotics, RoboCup attracts ordinary people, especially kids, high school and university students. Over 3000 people from 35 nations around the world have participated in RoboCup since the great success of the First Robot World Cup Soccer Games and Conferences, RoboCup-97 [1] held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97). Every year, the number of participating teams is increasing about 50%, that is, 35 teams in RoboCup- 97, 64 teams in RoboCup-98 [2], and 90 teams in RoboCup-99 [3], and almost same number of teams in RoboCup-2000. Attendance in 2000 was impacted by the application of a new qualification process, difficulties for some of the European teams to travel to Australia, and by the addition of a European RoboCup competition, EURO-2000. This paper focuses on a discussion of the challenging research problems present in RoboCup and how they have been concretely addressed in RoboCup competitions in 2000.
35452438
Learning situation-dependent costs: Improving planning from probabilistic robot execution
Abstract Physical domains are notoriously hard to model completely and correctly, especially to capture the dynamics of the environment. In this article, we present Rogue , a robot that learns from its execution experiences. Since actions may have different costs under different conditions, we introduce the concept of situation-dependent rules, in which situational features are attached to the costs or probabilities, reflecting patterns and dynamics encountered in the environment. Rogue extracts learning opportunities from massive, continual, probabilistic execution traces. It then correlates these learning opportunities with environmental features, creating situation-dependent costs for its actions. We present the development and use of these rules for a robotic path planner. Our empirical results show that situation-dependent rules effectively improve the planner’s model of the environment, thus allowing the planner to predict and avoid failures, to create plans that are tailored to the real world, and to respond to a changing environment.