Knowledge Management Supporting Autonomous Logistic Processes (2004 - 2011)

In analogy to conventional logistics, autonomous logistic processes are in need of knowledge to perform their task. Data, information, and knowledge are the key resources which ensure the quality of the logistic process. Knowledge management is required to support autonomous logistic processes by context-sensitive provision of knowledge. Furthermore, it has to be considered, that actors in these processes act in a competitive way. Consequently, information and knowledge should be treated as tradable goods which potentially have a high utility for their consumers.

Results

Starting with a systematic analysis of knowledge-oriented system architectures, the research within this subproject identified requirements for knowledge management in autonomous processes. An approach based on roles and parameters has been developed to enable knowledge management in this domain. The distribution is implemented by flexible agent interaction mechanisms. Roles are complex behaviour patterns which are incorporated by agents. By the help of these roles, complex knowledge management functionalities, e.g., retrieval, distribution, and requests, are enabled. Details of this approach have been presented and published on international workshops and conferences. Furthermore, an ontology as the formal representation of domain knowledge has been developed, which provides the basic concepts for the specification of transport, information, and manufacturing logistics. The SFB 637-wide multiagent-based simulation platform PlaSMA has been specified and prototypically implemented. This platform may be used for the analysis of logistic systems and evaluation of autonomous logistic processes by agent-based simulation.

In the second phase of the SFB 637 , the project focused on the subject area of context awareness. To this end, it is initially investigated which pieces of context information bear relevance for autonomous logistic systems in concrete situations where decisions need to be rendered. It is further investigated how, based on a quantification of the aforementioned relevance, a goal-oriented acquisition of required information can be enabled. Moreover, relevant pieces of information are often not immediately available via local sensors or offered by information services. For such cases, the applicability of machine learning, conjointly conducted by the autonomous systems, has been subject of further research.

Approach

The development of a physical world model for the PlaSMA system enabled further experiments regarding the assessment of information by the agents with respect to its value in the decision making process. Consequently, the influence of the accessibility of environmental information on the decision making was quantified in corresponding simulations. Additionally both logic-based and probabilistic forms of knowledge representations were examined in terms of their suitability for dealing with partial and uncertain knowledge. Based on these findings an approach for optimized information acquisition was developed. To enhance the formal knowledge representations employed Foundational Ontologies were investigated in terms of their suitability for representing context-dependent knowledge. This formed the basis for the reification of concepts and, thus, enabled a situation-based interpretation of environment information. In close cooperation with the project B10 machine learning methods were examined for local prediction models based on individual experiences for autonomous adaptation. These methods were then adapted for predicting and optimizing individual operative tour planning.

Prof. Dr. Otthein Herzog
Universität Bremen
TZI - Center for Computing and Communication Technologies
Intelligent Systems Department

Am Fallturm 1, 28359 Bremen, Germany
Phone: +49 421 218 64003, Fax: +49 421 218 64047

E-Mail: herzog(at)tzi.de

Contact

Dipl.-Inf. Tobias Warden
Universität Bremen
TZI - Center for Computing and Communication Technologies
Intelligent Systems Department

Am Fallturm 1, 28359 Bremen, Germany
Phone: +49 421 218 64027, Fax: +49 421 218 64047

E-Mail: warden(at)tzi.de