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

Liveness of extended control structure nets

Journal ArticleA new subclass of Petri nets is defined allowing the control structure representation of parallel programs including arbitrary semaphore operations. Structural properties of the "Extended Control Structure Nets" are discussed and seven necessary and sufficient conditions for their liveness are proved

Qualitative Abstraction and Inherent Uncertainty in Scene Recognition

The interpretation of scenes, e.g., in videos, is demanding at all levels. At the image processing level it is necessary to apply an "intelligent" segmentation and to determine the objects of interest. For the higher symbolic levels it is a challenging task to perform the transition between quantitative and qualitative data and to determine the relations between objects. Here we assume that the position of objects ("agents") in images and videos will already be determined as a minimal requirement for the further analysis. The interpretation of complex and dynamic scenes with embedded intentional agents is one of the most challenging tasks in current AI and imposes highly heterogeneous requirements. A key problem is the efficient and robust representation of uncertainty. We propose that uncertainty should be distinguished with respect to two different epistemological sources: (1) noisy sensor information and (2) ignorance. In this presentation we propose possible solutions to this class of problems. The use and evaluation of sensory information in the field of robotics shows impressive results especially in the fields of localization (e.g. MCL) and map building (e.g. SLAM) but also imposes serious problems on the successive higher levels of processing due to the probabilistic nature. In this presentation we propose that the use of (a) qualitative abstraction (classic approach) from quantitative to (at least partial) qualitative representations and (b) coherence-based perception validation based on Dempster-Shafer (DST) can help to reduce the problem significantly. The second important probability problem class that will be addressed is ignorance. In our presentation we will focus on reducing missing information by inference. We contrast/compare our experiences in an important field of scene interpretation namely plan and intention recognition. The first approach is based on a logical abductive approach and the second approach in contrast uses a probabilistic approach (Relational Hidden Markov Model (RHMM))

Towards Flexible and High-Level Modeling and Enacting of Processes

Process modeling and enacting concepts are at the center of workflow management. Support for heterogeneous processes, flexibility, reuse, and distribution are great challenges for the design of the next generation process modeling languages and their enactment mechanisms. Furthermore, flexible and collaborative processes depend also on unpredictable changes and hence require human intervention. Therefore, high-level process modeling constructs are needed which allow for an easy, adequate, and participatory design of workflows. We present a process modeling language which covers these requirements and is based on object-oriented modeling and enacting techniques. In particular, we outline how tasks and task nets are specified at a high level of abstraction, how flexible and user-adaptable control and data flow specifications are supported, and how reuse of workflow models can be improved. The approach is characterized by the uniform and integrated modeling of workflow sche..

Managing Evolving Workflow Specifications With Schema Versioning and Migration Rules

. Dynamic evolution and adaptation of workflow models due to process (re)engineering activities and dynamic changing situations of the real process is one of the most important challenges in workflow management. In this paper, we present an approach for the management of evolving workflow specifications which copes with the evolution of a workflow schema and the dynamic modification of workflow instances. The approach is based on the integrated mod- eling of workflow schema and instance elements, the separated definition of `what to do' and `how to do' in the workflow schema in conjunction with late binding of a workflow at run-time, the versioning of the workflow schema, and capabilities for defining complex workflow migration rules by adopting graph replacement rules. On this basis, we support different propagation /migration strategies as well as local adjustment of instances and their upward propagation. Furthermore, we address the problem of managing consistent configurations of ..

Mining Temporal Patterns from Relational Data

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