Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

Temporal Pattern Mining in Dynamic Environments

Dynamic scenes with many different objects and interrelations changing over time demand complex representations. The identification of frequent patterns and prediction rules in such scenes would be very valuable as associations in the data could be discovered or a system’s performance could even be improved by utilizing the new information in the behavior decision process. In this work, a novel approach to temporal pattern mining in dynamic environments has been proposed.

Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

Mining Temporal Patterns from Relational Data

Agents in dynamic environments have to deal with world representations that change over time

Qualitative Mapping of Sensory Data for Intelligent Vehicles

Recent advances in the field of intelligent vehicles have shown the applicability and utility for driver assistance systems, or even letting a car drive autonomously on highways. Usually these approaches are on a rather quantitative level. This hampers their capability to cope situations of great complexity in which humans need a lot of knowledge to act safely, for instance in city traffic. A qualitative representation of traffic scenes allows for formulating and using common sense knowledge in a human-comprehensible and machineprocessable way. A vocabulary for such a representation is proposed and a prototype that does the qualitative abstraction for knowledge-based behaviour control is presented and evaluated. Experiments in a simulation environment show the applicability of the approach for intelligent vehicles

Reflection and Norms: Towards a Model for Dynamic Adaptation for MAS

The design of self-organizing systems and particular multiagent systems (MAS) is a non trivial task. On the one hand the particular system should show a dynamic behavior according to its environment, to gain a central advantage of distributed systems, on the other hand it has to act on behalf of its user and the final results have to possess acceptable quality. Especially the quality of the overall system\u27s behavior can become a critical issue, if the subsystems have their own objectives they have to optimize. In this paper we present a methodology that can be integrated into MAS for adapting their behavior allowing local optimization while respecting an acceptable level of the system\u27s global goals

A knowledge-based approach to behavior decision in intelligent vehicles

Recent advances in the field of intelligent vehicles have shown that it is possible nowadays to provide the driver with useful assistance systems, or even letting a car drive autonomously over long distances on highways. Usually these approaches are on a rather quantitative level. A knowledgebased approach as presented here has the advantage of a better comprehensibility and allows for formulating and using common sense knowledge and traffic rules while reasoning. In our approach a knowledge base is the central component for higherlevel functionality. A qualitative mapping module abstracts from the quantitative data and stores symbolic facts in the knowledge base. The knowledge-based approach allows for easily integrating and adjusting background knowledge. Higher-level modules can query the knowledge base in order to evaluate the situation and decide what actions to perform. For the evaluation of the approach a prototype was developed in order to simulate traffic scenarios. In experiments behavior decision was applied for controlling the vehicle and its gaze