The coverage of a Region of Interest (RoI), that must be satisfied when deploying a Wireless Sensor Network (WSN), depends on several factors related not only to the sensor nodes (SNs) capabilities but also to the RoI topography. This latter has been omitted by most previous deployment approaches, which assume that the RoI is 2D. However, some recent WSNs deployment approaches dropped this unrealistic assumption. This paper surveys the different models adopted by the state-of-the-art deployment approaches. The weaknesses that need to be addressed are identified and some proposals expected to enhance the practicality of these models are discussed.
The aim of the paper is to extend our formal model of persuasion with an aspect of change of uncertainty interpreted probabilistically. The general goal of our research is to apply this model to design a logic and a software tool that allow for verification of persuasive multi-agent systems (MAS). To develop such a model, we analyze and then adopt the Probabilistic Dynamic Epistemic Logic introduced by B. Kooi. We show that the extensions proposed in this paper allow us to represent selected aspects of persuasion and apply the model in the resource re-allocation problem in multi-agent systems.
PL
Celem pracy jest rozszerzenie zaproponowanego przez nas formalnego modelu perswazji o aspekt zmiany niepewności przekonań agentów interpretowanych w teorii prawdopodobieństwa. Wzbogacony model jest podstawą do zdefiniowania logiki i zaprojektowania narzędzia, które umożliwia automatyczną weryfikację perswazyjnych systemów wieloagentowych. W celu realizacji tego zadania analizujemy i adaptujemy Probabilistyczną Dynamiczną Epistemiczną Logikę wprowadzoną przez B. Kooi. Zastosowanie zaproponowanego podejścia do analizowania wybranych aspektów perswazji omawiamy na przykładzie problemu alokacji zasobów w rozproszonych komputerowych systemach.
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Incidence calculus is a probabilistic logic which possesses both numerical and symbolic approaches. However, Liu in [5] pointed out that the original incidence calculus had some drawbacks and she established a generalized incidence calculus theory (GICT) based on ukasiewicz's three-valued logic to improve it. In a GICT, an incidence function is defined to relate each proposition f in the axioms of the theory to a set of possible worlds in which f has truth value true. But the incidence function only represents those absolute true states of propositions, so it can not deal with the uncertain states. In this paper, we use two incidence functions i* and i* to relate the axioms to the sets of possible worlds. For an axiom f, i*(f) is to be thought of as the set of possible worlds in which f has truth value true, while i*(f) is the set of possible worlds in which f is true or undeterminable. Since i* can represent the undeterminable state, our newly defined theory is more efficient to handle vague information than GICT.
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