Results


Reports of the AAAI 2009 Spring Symposia

AI Magazine

The titles of the nine symposia were Agents that Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real-World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Intelligent Event Processing symposium discussed the need for more AI-based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain.


Reports of the AAAI 2009 Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23–25, 2009 at Stanford University. The titles of the nine symposia were Agents that Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real-World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents that Learn from Human Teachers was to investigate how we can enable software and robotics agents to learn from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more AI-based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic Web symposium focused on the real-world grand challenges in this area. Finally, the Technosocial Predictive Analytics symposium explored new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models.


The CS Freiburg Team: Playing Robotic Soccer Based on an Explicit World Model

AI Magazine

Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model.


The CS Freiburg Team: Playing Robotic Soccer Based on an Explicit World Model

AI Magazine

Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Moreover, problems and solutions can easily be communicated because soccer is a well-known game. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model. As has been demonstrated by the performance of our team, we were successful with the first two points. Moreover, robotic soccer gave us the opportunity to study problems in distributed, cooperative sensing.