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HS-IKI-TR-08-001
Title: Verifying an industrial system using REX
Author: AnnMarie Ericsson
Abstract:
The use of formal methods for enhancing software quality is still not used in its full potential in industry. We argue that seamless support in a high-level specification tool is a viable way to provide industrial system designers with complex and powerful formal verification techniques. The REX tool supports specification of applications constructed as a set of rules and complex events. REX provides seamless support for specifying and verifying application specific requirement properties in the timed automata model-checking tool Uppaal. The rules, events and requirements of an application design is automatically transformed to a timed automaton representation and verified in the Uppaal tool. In order to validate the applicability of our approach, we present experimental results from a case-study of an industrial system. Based on the case-study results, we conclude that complex applications can be efficiently verified using our approach.
HS-IKI-TR-08-002
Title: Investigating human-computer interaction issues in information-fusion-based decision support
Authors: Maria Nilsson, Maria Riveiro, Tom Ziemke
Abstract:
Information fusion is a research area which focuses on how to combine information from many different sources to support decision making. Commonly used information fusion systems are often complex and used in military and crises management domains. The focus of information fusion research so far has been mainly on the technological aspects. There is still a lack of understanding relevant user aspects that affect the information fusion systems as a whole. This paper presents a framework of HCI issues which considers users as embedded in the context of information fusion systems. The framework aims at providing insights regarding factors that affect user interaction to inform the development of future information fusion systems. Design considerations are presented together with a heuristic evaluation of an information fusion prototype.
HS-IKI-TR-08-003
Title: Evaluating Credal Set Theory as a Belief Framework in High-Level Information Fusion for Automated Decision-Making
Author: Alexander Karlsson
Abstract:
The goal of high-level information fusion is to provide effective decision-support regarding situations, e.g., relations between events. One of the main ways that has been proposed in order to achieve this is to reduce uncertainty regarding the situation by utilizing multiple sources of information. There exist two types of uncertainty: aleatory and epistemic. Aleatory uncertainty, also known as uncertainty due to chance, cannot be reduced regardless of the amount of information. Epistemic uncertainty, on the other hand, also known as uncertainty due to lack of information, can be reduced if more information becomes available. Since the goal of high-level information fusion states that we want to reduce uncertainty by utilizing information, we conclude that the type of uncertainty
referred to is epistemic in nature. Uncertainty in high-level information fusion is most often expressed via a belief framework. The most common such framework in high-level information fusion is precise Bayesian theory. In this thesis proposal we argue that precise Bayesian theory cannot adequately represent epistemic uncertainty and that there exists another belief framework referred to as credal set theory that possesses this ability. This can actually be demonstrated by such simple as tossing a coin. In precise Bayesian theory, assuming no prior information about the coin, the same probability of "Head" can be adopted as the belief before any information is available, as a prior, as well as later when a large amount of information is available, as a posterior. By utilizing credal set theory, where a credal set is defined as a closed convex set of probability measures, this case amounts to representing the prior of "Head" as a probability interval, and a posterior with a smaller interval. The idea is that when a large amount of information is available, the interval converges into a point, i.e., the length of the interval, or degree of imprecision, reflects the degree of epistemic uncertainty. In precise Bayesian theory, a common automated decision-making strategy is to decide for the action that maximizes the expected utility with respect to a utility function and a probability measure. Since the probability measure cannot adequately reflect the amount of information of which it is based on, this is an approach that does not take epistemic uncertainty into consideration, i.e., it is
possible to decide for an action based on a high degree of epistemic uncertainty, and not even be aware of it. By utilizing credal set theory, epistemic uncertainty is reflected by imprecision in both probabilities and expected utilities. The main problem addressed in this thesis proposal is to decide if better automated decisions can be made by utilizing credal set theory as a belief framework in high-level information fusion, in comparison to precise Bayesian theory. The research question addressed is whether it is possible to characterize, in terms of degree of epistemic uncertainty, when and why one framework is better suited than the other for this purpose.
HS-IKI-TR-08-004
Title: Information Fusion from Databases, Sensors and Simulations. Annual Report 2007.
Authors: Sten F. Andler, Marcus Brohede (Eds.)