A formal approach to modelling and verification of context-aware systems

The evolution of smart devices and software technologies has expanded the domain of computing from workplaces to other areas of our everyday life. This trend has been rapidly advancing towards ubiquitous computing environments, where smart devices play an important role in acting intelligently on behalf of the users. One of the sub fields of the ubiquitous computing is context-aware systems. In context-aware systems research, ontology and agent-based technology have emerged as a new paradigm for conceptualizing, designing, and implementing sophisticated software systems. These systems exhibit complex adaptive behaviors, run in highly decentralized environment and can naturally be implemented as agent-based systems. Usually context-aware systems run on tiny resource-bounded devices including smart phones and sensor nodes and hence face various challenges. The lack of formal frameworks in existing research presents a clear challenge to model and verify such systems. This thesis addresses some of these issues by developing formal logical frameworks for modelling and verifying rule-based context-aware multi-agent systems. Two logical frameworks LOCRS and LDROCS have been developed by extending CTL* with belief and communication modalities, which allow us to describe a set of rule-based context-aware reasoning agents with bound on time, memory and communication. The key idea underlying the logical approach of context-aware systems is to define a formal logic that axiomatizes the set of transition systems, and it is then used to state various qualitative and quantitative properties of the systems. The set of rules which are used to model a desired system is derived from OWL 2 RL ontologies. While LOCRS is based on monotonic reasoning where beliefs of an agent cannot be revised based on some contradictory evidence, the LDROCS logic handles inconsistent context information using non-monotonic reasoning. The modelling and verification of a healthcare case study is illustrated using Protégé IDE and Maude LTL model checker

A formal approach to modelling and verification of context-aware systems

The evolution of smart devices and software technologies has expanded the domain of computing from workplaces to other areas of our everyday life. This trend has been rapidly advancing towards ubiquitous computing environments, where smart devices play an important role in acting intelligently on behalf of the users. One of the sub fields of the ubiquitous computing is context-aware systems. In context-aware systems research, ontology and agent-based technology have emerged as a new paradigm for conceptualizing, designing, and implementing sophisticated software systems. These systems exhibit complex adaptive behaviors, run in highly decentralized environment and can naturally be implemented as agent-based systems. Usually context-aware systems run on tiny resource-bounded devices including smart phones and sensor nodes and hence face various challenges. The lack of formal frameworks in existing research presents a clear challenge to model and verify such systems. This thesis addresses some of these issues by developing formal logical frameworks for modelling and verifying rule-based context-aware multi-agent systems. Two logical frameworks LOCRS and LDROCS have been developed by extending CTL* with belief and communication modalities, which allow us to describe a set of rule-based context-aware reasoning agents with bound on time, memory and communication. The key idea underlying the logical approach of context-aware systems is to define a formal logic that axiomatizes the set of transition systems, and it is then used to state various qualitative and quantitative properties of the systems. The set of rules which are used to model a desired system is derived from OWL 2 RL ontologies. While LOCRS is based on monotonic reasoning where beliefs of an agent cannot be revised based on some contradictory evidence, the LDROCS logic handles inconsistent context information using non-monotonic reasoning. The modelling and verification of a healthcare case study is illustrated using Protégé IDE and Maude LTL model checker

On the cost-complexity of multi-context systems

Multi-context systems provide a powerful framework for modelling information-aggregation systems featuring heterogeneous reasoning components. Their execution can, however, incur non-negligible cost. Here, we focus on cost-complexity of such systems. To that end, we introduce cost-aware multi-context systems, an extension of non-monotonic multi-context systems framework taking into account costs incurred by execution of semantic operators of the individual contexts. We formulate the notion of cost-complexity for consistency and reasoning problems in MCSs. Subsequently, we provide a series of results related to gradually more and more constrained classes of MCSs and finally introduce an incremental cost-reducing algorithm solving the reasoning problem for definite MCSs

Arguing Using Opponent Models

Peer reviewedPostprin

A model and architecture for situation determination

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. Furthermore, situations are commonly recognised at a low-level of granularity, which limits the scope of situation-aware applications. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. A proposed architecture of an accompanying situation determination middleware is provided, as well as an analysis of a prototype implementation

Logic-Based Specification Languages for Intelligent Software Agents

The research field of Agent-Oriented Software Engineering (AOSE) aims to find abstractions, languages, methodologies and toolkits for modeling, verifying, validating and prototyping complex applications conceptualized as Multiagent Systems (MASs). A very lively research sub-field studies how formal methods can be used for AOSE. This paper presents a detailed survey of six logic-based executable agent specification languages that have been chosen for their potential to be integrated in our ARPEGGIO project, an open framework for specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each executable language, the logic foundations are described and an example of use is shown. A comparison of the six languages and a survey of similar approaches complete the paper, together with considerations of the advantages of using logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal "Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe Editor-in-Chie

Bayesianism for Non-ideal Agents

Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience within a Bayesian framework. Some proposals merely replace logical omniscience with a different logical idealization; others sacrifice all traits of logical competence on the altar of logical non-omniscience. We think a better strategy is available: by enriching the Bayesian framework with tools that allow us to capture what agents can and cannot infer given their limited cognitive resources, we can avoid logical omniscience while retaining the idea that rational degrees of belief are in an important way constrained by the laws of probability. In this paper, we offer a formal implementation of this strategy, show how the resulting framework solves the problem of logical omniscience, and compare it to orthodox Bayesianism as we know it

Reasoning about Action: An Argumentation - Theoretic Approach

We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper

Intentions and Information in Discourse

This paper is about the flow of inference between communicative intentions, discourse structure and the domain during discourse processing. We augment a theory of discourse interpretation with a theory of distinct mental attitudes and reasoning about them, in order to provide an account of how the attitudes interact with reasoning about discourse structure

Reason Maintenance - State of the Art

This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques