No abstract

No abstract

No abstract

This paper proposes a novel hierarchical multi-layer decision tree for representing reactive robot navigation knowledge. In this representation, the perception space is decomposed into a hierarchical set of worlds reflecting environments which are homogeneous in nature and which vary in complexity in an ordered manner. Each world is used to produce a corresponding decision tree which is trained incrementally. The instantaneous perception of the robot is used to select an appropriate rule from the decision tree and a sequence of rule activations form the complete trajectory. The ability to keep the knowledge complexity manageable and under control is an important aspect of the technique.

This paper proposes a new multi-strategy (hybrid)intelligent control technique whose concept is applicable to the control of a wide range of processes. The proposed technique uses Incremental Tree Induction (ITI) as the learning algorithm, and incorporates fuzzy logic to deal with uncertainties apparent in the process to be controlled. ITI operates solely on symbolic fuzzy knowledge, as both the input features (data obtained from the process to be controlled) and the output decisions of the intelligent controller are described by fuzzy linguistic variables. Fuzzy associative memories (FAMs) are employed to store and manage the fuzzy knowledge, and are simulated operationally by fuzzy binary decision trees which encode the input-output space. The main novelty of this approach is the automatic synthesis (both off-line and on-line) of multi-dimensional FAMs from inception, in contrast to the manual implementation of traditional FAMs. A second important advantage is the self-explanatory nature of the FAMs (and their underlying control laws) generated by this approach. The new technique is demonstrated in its application to the intelligent navigation of a mobile robot.