Optimization and inference under fuzzy numerical constraints

Εκτεταμένη έρευνα έχει γίνει στους τομείς της Ικανοποίησης Περιορισμών με διακριτά (ακέραια) ή πραγματικά πεδία τιμών. Αυτή η έρευνα έχει οδηγήσει σε πολλαπλές σημασιολογικές περιγραφές, πλατφόρμες και συστήματα για την περιγραφή σχετικών προβλημάτων με επαρκείς βελτιστοποιήσεις. Παρά ταύτα, λόγω της ασαφούς φύσης πραγματικών προβλημάτων ή ελλιπούς μας γνώσης για αυτά, η σαφής μοντελοποίηση ενός προβλήματος ικανοποίησης περιορισμών δεν είναι πάντα ένα εύκολο ζήτημα ή ακόμα και η καλύτερη προσέγγιση. Επιπλέον, το πρόβλημα της μοντελοποίησης και επίλυσης ελλιπούς γνώσης είναι ακόμη δυσκολότερο. Επιπροσθέτως, πρακτικές απαιτήσεις μοντελοποίησης και μέθοδοι βελτιστοποίησης του χρόνου αναζήτησης απαιτούν συνήθως ειδικές πληροφορίες για το πεδίο εφαρμογής, καθιστώντας τη δημιουργία ενός γενικότερου πλαισίου βελτιστοποίησης ένα ιδιαίτερα δύσκολο πρόβλημα. Στα πλαίσια αυτής της εργασίας θα μελετήσουμε το πρόβλημα της μοντελοποίησης και αξιοποίησης σαφών, ελλιπών ή ασαφών περιορισμών, καθώς και πιθανές στρατηγικές βελτιστοποίησης. Καθώς τα παραδοσιακά προβλήματα ικανοποίησης περιορισμών λειτουργούν βάσει συγκεκριμένων και προκαθορισμένων κανόνων και σχέσεων, παρουσιάζει ενδιαφέρον η διερεύνηση στρατηγικών και βελτιστοποιήσεων που θα επιτρέπουν το συμπερασμό νέων ή/και αποδοτικότερων περιορισμών. Τέτοιοι επιπρόσθετοι κανόνες θα μπορούσαν να βελτιώσουν τη διαδικασία αναζήτησης μέσω της εφαρμογής αυστηρότερων περιορισμών και περιορισμού του χώρου αναζήτησης ή να προσφέρουν χρήσιμες πληροφορίες στον αναλυτή για τη φύση του προβλήματος που μοντελοποιεί.Extensive research has been done in the areas of Constraint Satisfaction with discrete/integer and real domain ranges. Multiple platforms and systems to deal with these kinds of domains have been developed and appropriately optimized. Nevertheless, due to the incomplete and possibly vague nature of real-life problems, modeling a crisp and adequately strict satisfaction problem may not always be easy or even appropriate. The problem of modeling incomplete knowledge or solving an incomplete/relaxed representation of a problem is a much harder issue to tackle. Additionally, practical modeling requirements and search optimizations require specific domain knowledge in order to be implemented, making the creation of a more generic optimization framework an even harder problem.In this thesis, we will study the problem of modeling and utilizing incomplete and fuzzy constraints, as well as possible optimization strategies. As constraint satisfaction problems usually contain hard-coded constraints based on specific problem and domain knowledge, we will investigate whether strategies and generic heuristics exist for inferring new constraint rules. Additional rules could optimize the search process by implementing stricter constraints and thus pruning the search space or even provide useful insight to the researcher concerning the nature of the investigated problem

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

A Prioritized Assertional-Based Revision for DL-Lite Knowledge Bases

International audienc

Action, Time and Space in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation (KR) formalisms designed to represent and reason about static conceptual knowledge in a semantically well-understood way. On the other hand, standard action formalisms are KR formalisms based on classical logic designed to model and reason about dynamic systems. The largest part of the present work is dedicated to integrating DLs with action formalisms, with the main goal of obtaining decidable action formalisms with an expressiveness significantly beyond propositional. To this end, we offer DL-tailored solutions to the frame and ramification problem. One of the main technical results is that standard reasoning problems about actions (executability and projection), as well as the plan existence problem are decidable if one restricts the logic for describing action pre- and post-conditions and the state of the world to decidable Description Logics. A smaller part of the work is related to decidable extensions of Description Logics with concrete datatypes, most importantly with those allowing to refer to the notions of space and time

Modeling Trust in Multiagent Mobile Vehicular Ad-Hoc Networks through Enhanced Knowledge Exchange for Effective Travel Decision Making

This thesis explores how to effectively model trust in the environment of mobile vehicular ad-hoc networks. We consider each vehicle’s travel path planning to be guided by an intelligent agent that receives traffic reports from other agents in the environment. Determining the trustworthiness of these reports is thus a critical task. We take as a starting point the multi-dimensional trust model of Minhas et al. That work had a two-phased approach: i) model trust and ii) execute an algorithm for using that trust modeling, when deciding what route to take. The framework presented in this thesis aims to clarify i) the messaging that should be supported, ii) the internal representation of the messaging and the trust information and iii) the algorithms for sending and receiving information (thus updating knowledge) in order to perform decision making during route planning. A significant contribution is therefore offered through clarification and extension of the original trust modeling approach. In addition we design a comprehensive, extensive simulation testbed that is used to validate the effectiveness and robustness of the model. This testbed supports a variety of metrics and is able to perform testing in environments with a large number of cars. This constitutes the second significant contribution of the thesis. Overall, we present a valuable model for knowledge management in mobile vehicular ad-hoc networks through a combination of trust modeling, ontological representation of concepts and facts, and a methodology for discovering and updating user models. Included is a representation and implementation of both a push-based and pull-based messaging protocol. We also demonstrate the effectiveness of this model through validation conducted using our simulation testbed, focusing first on a subset of the multi-faceted trust model in order to highlight the value of the underlying representation, decision making algorithm and simulation metrics. One very valuable result is a demonstration of the importance of the combined use of the different dimensions employed in the trust modeling