Reasoning in Many Dimensions : Uncertainty and Products of Modal Logics

Probabilistic Description Logics (ProbDLs) are an extension of Description Logics that are designed to capture uncertainty. We study problems related to these logics. First, we investigate the monodic fragment of Probabilistic first-order logic, show that it has many nice properties, and are able to explain the complexity results obtained for ProbDLs. Second, in order to identify well-behaved, in best-case tractable ProbDLs, we study the complexity landscape for different fragments of ProbEL; amongst others, we are able to identify a tractable fragment. We then study the reasoning problem of ontological query answering, but apply it to probabilistic data. Therefore, we define the framework of ontology-based access to probabilistic data and study the computational complexity therein. In the final part of the thesis, we study the complexity of the satisfiability problem in the two-dimensional modal logic KxK. We are able to close a gap that has been open for more than ten years

μ計算と算術の階層構造の探索---ゲール・スチュワートゲームの視点より

Tohoku University横山啓太課

Glosarium Matematika

273 p.; 24 cm

Points de vue sur les croyances et leur changement

In this thesis, we propose logical models for belief representation and belief change in a multi-agent setting, stressing the importance of choosing a particular modeling point of view. In that respect, we distinguish two approaches: the external approach, where the modeler is somebody external to the situation; the internal approach, where the modeler is one of the agents. We propose an internal version of dynamic epistemic logic (with event models) which allows us to generalize easily AGM belief revision theory to the multi-agent case. Afterwards, we model the complex logical dynamics underlying the interpretation of events by adding probabilities and infinitesimals. Finally we propose an alternative without using event models by introducing instead a converse event operator.Dans cette thèse, nous proposons des modèles logiques pour la représentation des croyances et leur changement dans un cadre multi-agent, en insistant sur l'importance de se fixer un point de vue particulier pour la modélisation. A cet égard, nous distinguons deux approches différentes: l'approche externe, où le modélisateur est quelqu'un d'externe à la situation; l'approche interne, où le modélisateur est l'un des agents. Nous proposons une version interne de la logique épistémique dynamique (avec des modèles d'événements), ce qui nous permet de généraliser facilement la théorie de la révision des croyances d'AGM au cas multi-agent. Ensuite, nous mod´elisons les dynamismes logiques complexes qui soustendent notre interprétation des événements en introduisant des probabilités et des infinitésimaux. Finalement, nous proposons un formalisme alternatif qui n'utilise pas de modèle d'événement mais qui introduit à la place un opérateur d'événement inverse

Dynamical systems techniques in the analysis of neural systems

As we strive to understand the mechanisms underlying neural computation, mathematical models are increasingly being used as a counterpart to biological experimentation. Alongside building such models, there is a need for mathematical techniques to be developed to examine the often complex behaviour that can arise from even the simplest models. There are now a plethora of mathematical models to describe activity at the single neuron level, ranging from one-dimensional, phenomenological ones, to complex biophysical models with large numbers of state variables. Network models present even more of a challenge, as rich patterns of behaviour can arise due to the coupling alone. We first analyse a planar integrate-and-fire model in a piecewise-linear regime. We advocate using piecewise-linear models as caricatures of nonlinear models, owing to the fact that explicit solutions can be found in the former. Through the use of explicit solutions that are available to us, we categorise the model in terms of its bifurcation structure, noting that the non-smooth dynamics involving the reset mechanism give rise to mathematically interesting behaviour. We highlight the pitfalls in using techniques for smooth dynamical systems in the study of non-smooth models, and show how these can be overcome using non-smooth analysis. Following this, we shift our focus onto the use of phase reduction techniques in the analysis of neural oscillators. We begin by presenting concrete examples showcasing where these techniques fail to capture dynamics of the full system for both deterministic and stochastic forcing. To overcome these failures, we derive new coordinate systems which include some notion of distance from the underlying limit cycle. With these coordinates, we are able to capture the effect of phase space structures away from the limit cycle, and we go on to show how they can be used to explain complex behaviour in typical oscillatory neuron models

Dynamical systems techniques in the analysis of neural systems

As we strive to understand the mechanisms underlying neural computation, mathematical models are increasingly being used as a counterpart to biological experimentation. Alongside building such models, there is a need for mathematical techniques to be developed to examine the often complex behaviour that can arise from even the simplest models. There are now a plethora of mathematical models to describe activity at the single neuron level, ranging from one-dimensional, phenomenological ones, to complex biophysical models with large numbers of state variables. Network models present even more of a challenge, as rich patterns of behaviour can arise due to the coupling alone. We first analyse a planar integrate-and-fire model in a piecewise-linear regime. We advocate using piecewise-linear models as caricatures of nonlinear models, owing to the fact that explicit solutions can be found in the former. Through the use of explicit solutions that are available to us, we categorise the model in terms of its bifurcation structure, noting that the non-smooth dynamics involving the reset mechanism give rise to mathematically interesting behaviour. We highlight the pitfalls in using techniques for smooth dynamical systems in the study of non-smooth models, and show how these can be overcome using non-smooth analysis. Following this, we shift our focus onto the use of phase reduction techniques in the analysis of neural oscillators. We begin by presenting concrete examples showcasing where these techniques fail to capture dynamics of the full system for both deterministic and stochastic forcing. To overcome these failures, we derive new coordinate systems which include some notion of distance from the underlying limit cycle. With these coordinates, we are able to capture the effect of phase space structures away from the limit cycle, and we go on to show how they can be used to explain complex behaviour in typical oscillatory neuron models