Actors, actions, and initiative in normative system specification

The logic of norms, called deontic logic, has been used to specify normative constraints for information systems. For example, one can specify in deontic logic the constraints that a book borrowed from a library should be returned within three weeks, and that if it is not returned, the library should send a reminder. Thus, the notion of obligation to perform an action arises naturally in system specification. Intuitively, deontic logic presupposes the concept of anactor who undertakes actions and is responsible for fulfilling obligations. However, the concept of an actor has not been formalized until now in deontic logic. We present a formalization in dynamic logic, which allows us to express the actor who initiates actions or choices. This is then combined with a formalization, presented earlier, of deontic logic in dynamic logic, which allows us to specify obligations, permissions, and prohibitions to perform an action. The addition of actors allows us to expresswho has the responsibility to perform an action. In addition to the application of the concept of an actor in deontic logic, we discuss two other applications of actors. First, we show how to generalize an approach taken up by De Nicola and Hennessy, who eliminate from CCS in favor of internal and external choice. We show that our generalization allows a more accurate specification of system behavior than is possible without it. Second, we show that actors can be used to resolve a long-standing paradox of deontic logic, called the paradox of free-choice permission. Towards the end of the paper, we discuss whether the concept of an actor can be combined with that of an object to formalize the concept of active objects

A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

Abstract: High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved

Conflict Resolution using Derived Classes

Common object models select method implementations based on the class the receiver belongs to. If an object belongs to several classes without a most specific one, more than one implementation is applicable for a method call. We present a conflict resolution strategy to get exactly one implementation for each call. This is achieved by adding suitable derived classes with method redefinitions

Extended RDF as a Semantic Foundation of Rule Markup Languages

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes