Development of Natural Induction Methodology for Discovering Patterns Supporting Autonomous Transportation (2008 - 2011)

This subproject had the goal to develop a methodology for automated discovery of traffic patterns from historic data characterizing the traffic in a given geographical region. The methodology will be based on natural induction approach that seeks computer knowledge in the forms natural to people, such as natural language-type descriptions and graphical representations. It will be implemented by extending the attributional rule learning system AQ21, and tested on the data provided by the SFB 637.

Objectives

Goals of this sub project, which is conducted in collaboration with the SFB 637 comprise the development of a methodology for the automated discovery of traffic patterns from historic data characterizing the traffic in a given geographical region, the design and implementation of appropriate software tools and the systematic evaluation of the methodology by means of multiagent-based simulation experiments in the PlaSMA system.

In logistics, control knowledge learned by an agent of course needs to be accurate. In addition, it also needs to be understandable and potentially modifiable by a human process expert. The rationale for the latter requirements, which are rarely satisfied by current learning systems, is that informed decisions which are rendered in autonomous control need to be comprehensible for human controllers. The representation of knowledge and models in a human-oriented form is thereby essential for the ability of software agents to justify their decision-making. Also, process-relevant knowledge may be validated and potentially complemented by a process expert.

Approach

To satisfy the aforementioned objectives to generate and represent knowledge in human-oriented forms, a particularly attractive learning approach is Natural Induction. It strives to discover patterns in presented data that are encoded in forms corresponding to simple natural-language statements.

The AQ21 rule learning system was utilized for discovery of traffic patterns in data collected by truck management agents over extended periods of time within a simulation environment. Conceived as a general-purpose tool to attributional classifiers from multi-class data, AQ21 needed to integrated with the PlaSMA simulation platform. The learning system also needed to be extended with regard to the handling of incomplete and ambiguous data. These steps then allowed for a detailed assessment of AQ21 learning performance and convergence behavior in logistic use cases.

In a complimentary strand of research, the Learnable Evolution Model (LEM), an approach for non-darwinian evolutionary optimization also developed at the MLI laboratory, was utilized as a means to optimize individual truck transport schedules.

Contact

Prof. Dr. Janusz Wojtusiak
Machine Learning and Inference Laboratory
Department of Health Administration and Policy
College of Health and Human Services
George Mason University

4400 University Drive, MSN 1J3, Fairfax, VA 22030, United States
Phone: +1 703 993 4148
E-Mail: jwojt@mli.gmu.edu