Designing Behaviour Based Systems Using the Space-time Distance Principle

This paper presents a methodology for constructing behaviour-based systems for use in safety critical applications. The specific requirements of constructing a rigorous safety argument for a system prior to its introduction into service tend to discourage design methods based on evolutionary and reinforcement learning techniques. A set of general design principles is proposed for construction of subsumption architecture systems, which can be applied deductively to identify behaviour modules and their organisation within the architecture. In addition, a general procedure for design of the behaviour module functions is also discussed. This design method is based on the use of Lyapunov stability techniques. An experiment is discussed, still in progress, which applies the methodology to a small behaviour-based system for an Inverted Pendulum apparatus. 1 Motivation for the Work. This paper is a review of a PhD project at the University of the West of England (UWE) Intelligent Autonomous Systems Lab, investigating design methods for behaviour-based autonomous agents that will allow their practical use in safety critical systems. The general philosophy underlying the behaviour-based approach has the potential to be very useful in safety critical systems design. Most currently proposed design methodologies for behaviour-based systems advocate a machine learning or evolutionary approach. Typically, an agent such as a mobile robot is built with few (if any) functions or rules governing its behaviour, and is then subjected to a series of experiments, in which the control laws for behaviour are built up through its built-in learning mechanism. Eventually the robot acquires the behaviour patterns needed to perform its required tasks. This is essentially a process of inductive reasoning; the system is built by developing general rules/laws of behaviour based on a specific set of examples. This approach is in marked contrast to traditional engineering methodologies, in which a system design is constructed by following a set of design rules or guidelines. The design is constructed (more or less) by a process of deductive reasoning, applying the general design rules to a set of initial system requirements that define the specific nature of the system. The system is then tested against its requirements to determine the extent to which the process was successful. Although evolutionary methodologies are a powerful means for producing design solutions for autonomous agents, there are problems with the philosophy of the approach that may prevent their use in safety critical systems. These systems require that a safety case be prepared prior to their use in service. A safety case provides a rationale to explain why the system should be considered acceptably safe. For systems that have the potential to cause very serious accidents, it is necessary to design and build the system, and develop its safety case, with the highest possible level of rigour. This ensures that safety regulators, owners, and users/customers of the system can have confidence that the system presents an acceptable risk. Proc. TIMR 01 Towards Intelligent Mobile Robots 2001. Technical Report Series, Department of Computer Science, Manchester University, ISSN 1361 6161 Report number UMCS-01-4-1. http://www.cs. man.ac. uk/csonly/cstechrep/titles01.html In general, scientific rigour is achieved through deductive reasoning. Popper [Ref.1] has demonstrated that scientific theories developed by means of inductive reasoning contain flaws where proof of the theory depends on proof of the inductive system of reasoning, which cannot be done without introducing logical inconsistencies. However, a deductive system reasoning, in which theories expressed in general terms (without reference to any specific instantiation of the problem) are subjected to falsification tests, are not subject to this logical flaw. With respect to the problem of designing systems, a scientifically rigorous methodology must develop solutions by deducing a design solution from general principles rather than inducing a solution from training examples. This contradicts the general philosophy of reinforcement learning techniques, and may require considerable discipline on the part of evolutionary development techniques if the same issue is to be avoided. If behaviour-based autonomous agents are ever to be used in safety critical industrial applications (and for robotic systems this may constitute the majority of foreseeable applications) then system design methodologies must be developed that incorporate a more deductive approach than many contemporary proposals, e.g. Cliff et al [Ref.2]. The development of such a methodology is the motivation of this work. 2 Selection of System Architecture. A number of architecture types have been reviewed for their suitability to safety critical systems applications. The original survey was based on references from the early 1990s [Refs.3, 4, 5]. However, Arkin [Ref.6] has recently published a comprehensive survey of behaviour-based research from the mid-1980's to the near-present (1998). The candidate architecture types were: 1. Brooks' Subsumption Architecture [Ref.7] 2. Connell's Subsumption Architecture [Ref.3] 3. Action-selection Networks [Ref.8] 4. Rosenblatt and Payton Architecture [Ref.9]