Ambient intelligence (AmI) proposes pervasive information systems composed of autonomous agents embedded within the environment who, in orchestration, complement human activity in an intelligent manner. As such, it is an interesting and challenging application area for many computer science fields and approaches. A critical issue in such application scenarios is that the agents must be able to acquire, exchange, and evaluate knowledge about the environment, its users, and their activities. Knowledge populated between the agents in such systems may be contextually dependent, ambiguous, and incomplete. Conflicts may thus naturally arise, that need to be dealt with by the agents in an autonomous way. In this survey, we relate AmI to the area of knowledge representation and reasoning (KR), where conflict resolution has been studied for a long time. We take a look at a number of KR approaches that may be applied: context modelling, multi-context systems, belief revision, ontology evolution and debugging, argumentation, preferences, and paraconsistent reasoning. Our main goal is to describe the state of the art in these fields, and to draw attention of researchers to important theoretical issues and practical challenges that still need to be resolved, in order to reuse the results from KR in AmI systems or similar complex and demanding applications.

While several interesting argumentation-based semantics for defeasible logic programs have been proposed, to our best knowledge, none of these approaches is able to fully handle the closure under strict rules in a sufficient manner: they are either not closed, or they use workarounds such as transposition of rules which violates desired directionality of logic programming rules. We propose a novel argumentation-based semantics, in which the status of arguments is determined by attacks between newly introduced conflict resolutions instead of attacks between arguments. We show that the semantics is closed w.r.t. strict rules and respects directionality of rules, as well as other desired properties previously published in the literature.

Abstract. We show how defeasible reasoning can be embedded into ABF. Differently from other proposals, we do not encode the conflict resolution mechanism for defeasible rules into the ABF's deductive systems. Instead, we formalize the notions of conflict and conflict resolution and make them part of the extended ABF framework (XABF). This improves the control over the conflict resolution process, and allows to devise and compare different domain-dependent conflict resolution strategies. We also show, that no matter which conflict resolution strategy is used, our framework is able to guarantee certain desired properties.

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<p><span class="fontstyle0">We describe a universal meteor simulation tool set named A</span><span class="fontstyle0">SMODEUS </span><span class="fontstyle0">and present several of its possible use cases. The toolset consists of a Monte-Carlo simulator of meteoroids entering the Earth</span><span class="fontstyle2">’</span><span class="fontstyle0">s atmosphere, functions for transformation to observer-centred coordinate frames representing virtual views of the sky, application of observational bias effects and a number of statistical tools for analyses of produced data sets and comparison to real-world data. The simulation has already been used in several areas of research, most notably estimates of meteoroid </span><span class="fontstyle3">fl</span><span class="fontstyle0">ux and de-biasing of real-world meteor observations and in investigation of how varying the initial properties of meteoroids affects the resulting meteors. It lends itself to many more possible applications, such as assessment of selection bias in ground-based observing systems, investigation of models of meteor </span><span class="fontstyle3">fl</span><span class="fontstyle0">ight and ablation, and evaluation of mass and population indices of meteor showers.</span></p>