Hierarchical Encapsulation and Abstraction Principle (heap) for Autonomous System Development

Abstract

A general approach t o task-based model development is summarized in a Hierarchical Encapsulation and Abstraction Principle (HEAP) and this principle is briefly illustrated in the planning, operations and diagnosis task domains. 1 Brief Overview on Model-Base Autonomous System Architecture To cope with complex objectives, an autonomous system requires integration of symbolic and numeric data, qualitative and quantitative information, reasoning and computation. A pure AI approach is too qualitatively oriented to handle quantitative information very well. For example, classic AI planning approaches [4, 5 , 61 do not consider the timing effects, which should be of primary concern in representing our dynamic world. On the other hand, control researchers have a fairly narrow view-point, so that they mainly focus on refinement rather than robustness of a system [7], and they usually consider only the normal operational aspects of a system. However, autonomous systems have to deal with abnormal behavior of a system as well. Thus, it is crucial to have a strong formalism and an environment that allows coherent integration of symbolic and numeric informations in a valid representation process to deal with a complex dynamic world. Approaches to design various autonomous component models for planning, operation, and diagnosis have previously been developed in their respective research fields so that there are many overlaps as well as inconsistencies in assumptions. In an integrated system, such components cannot be considered independently. For example, planning requires execution, and diagnosis is activated when anomalies are detected during execution. The model-based autonomous system architecture features a model base a t the center of its planning, operation, diagnosis, and fault recovery strategies [2]. In this way, it integrates AI symbolic models and controltheoretic dynamic models into a coherent system. Endomorphism refers to the existence of a homomorphism from an object to a sub-object within it, the part (sub-object) then being a model of the whole [8]. In order to control an object, a high autonomy system needs a corresponding model of the object to determine the particular action to take. The internal model used by the system and its world base model are related by abstraction, i.e., some form of homomorphic (i.e., endomorphic relation. The inference mation for interacting with the real world object. By “world base model” we mean the most comprehensive model of the world available to the system whether it exists as a single object or as a family of partial models in the model base. Typical expert systems comprise a domainindependent inference engine and a domain-dependent knowledge base. The inference engine examines the knowledge base and decides the order in which inferences are made. The engine-based modelling approach provides a clear separation between the domain -dependent model base and the domain-independent inference engine. It facilitates the automatic generation of a model base using endomorphisms. Figure l shows the engine-based modelling concept and examples of autonomous system components realized using the concept. engine asks its internal mo d el for the necessary infor-