Abstraction in directed model checking

Abstraction is one of the most important issues to cope with large and infinite state spaces in model checking and to reduce the verification efforts. The abstract system is smaller than the original one and if the abstract system satisfies a correctness specification, so does the concrete one. However, abstractions may introduce a behavior violating the specification that is not present in the original system. This paper bypasses this problem by proposing the combination of abstraction with heuristic search to improve error detection. The abstract system is explored in order to create a database that stores the exact distances from abstract states to the set of abstract error states. To check, whether or not the abstract behavior is present in the original system, effcient exploration algorithms exploit the database as a guidance

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described

HOW CAN PD PROCESS MODELLING BE MADE MORE USEFUL? AN EXPLORATION OF FACTORS WHICH INFLUENCE MODELLING UTILITY

In what sense is PD process modelling useful? and how can the utility of modelling be improved? In this paper, we approach these questions through an analysis of PD process modelling ‘utility’ – which in broad terms we consider to be the degree to which a model-based approach or modelling intervention benefits practice. We view the utility of modelling as a composite characteristic which depends both on the properties of models and on the way they are applied. The paper draws upon established principles of cybernetic systems in an attempt to explain the role played by process modelling in operating and improving PD processes. We use this framework to identify eight key factors which influence the utility of modelling in the context of use. Further, we indicate how these factors can be interpreted to identify opportunities to improve modelling utility.International Design Conference - DESIGN 201

Embodiment and embodied design

Picture this. A preverbal infant straddles the center of a seesaw. She gently tilts her weight back and forth from one side to the other, sensing as each side tips downward and then back up again. This child cannot articulate her observations in simple words, let alone in scientific jargon. Can she learn anything from this experience? If so, what is she learning, and what role might such learning play in her future interactions in the world? Of course, this is a nonverbal bodily experience, and any learning that occurs must be bodily, physical learning. But does this nonverbal bodily experience have anything to do with the sort of learning that takes place in schools - learning verbal and abstract concepts? In this chapter, we argue that the body has everything to do with learning, even learning of abstract concepts. Take mathematics, for example. Mathematical practice is thought to be about producing and manipulating arbitrary symbolic inscriptions that bear abstract, universal truisms untainted by human corporeality. Mathematics is thought to epitomize our species’ collective historical achievement of transcending and, perhaps, escaping the mundane, material condition of having a body governed by haphazard terrestrial circumstance. Surely mathematics is disembodied

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performance and increase failure analysis costs. In this paper, we present a novel behavior-guided fuzzing technique (BehAVExplor) to explore the different behaviors of the ego vehicle (i.e., the vehicle controlled by the ADS under test) and detect diverse violations. Specifically, we design an efficient unsupervised model, called BehaviorMiner, to characterize the behavior of the ego vehicle. BehaviorMiner extracts the temporal features from the given scenarios and performs a clustering-based abstraction to group behaviors with similar features into abstract states. A new test case will be added to the seed corpus if it triggers new behaviors (e.g., cover new abstract states). Due to the potential conflict between the behavior diversity and the general violation feedback, we further propose an energy mechanism to guide the seed selection and the mutation. The energy of a seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an industrial-level ADS, and LGSVL simulation environment. Empirical evaluation results show that BehAVExplor can effectively find more diverse violations than the state-of-the-art