Deriving Inverse Operators for Modal Logic

International audienceSpatial constraint systems are algebraic structures from concurrent constraint programming to specify spatial and epistemic behavior in multi-agent systems. We shall use spatial constraint systems to give an abstract characterization of the notion of normality in modal logic and to derive right inverse/reverse operators for modal languages. In particular, we shall identify the weakest condition for the existence of right inverses and show that the abstract notion of normality corresponds to the preservation of finite suprema. We shall apply our results to existing modal languages such as the weakest normal modal logic, Hennessy-Milner logic, and linear-time temporal logic. We shall discuss our results in the context of modal concepts such as bisimilarity and inconsistency invariance

Towards autonomous diagnostic systems with medical imaging

Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities. This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed. Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way. In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones. By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos. All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces

Towards Resolution-based Reasoning for Connected Logics

AbstractThe method of connecting logics has gained a lot of attention in the knowledge representation and ontology communities because of its intuitive semantics and natural support for modular KR, its generality, and its robustness concerning decidability preservation. However, so far no dedicated automated reasoning solutions have been developed, and the only reasoning available was via translation into sufficiently expressive logics. In this paper, we present a simple modalised version of basic E-connections, and develop a sound, complete, and terminating resolution-based reasoning procedure. The approach is modular and can be extended to more expressive versions of E-connections

Neuromodulatory Control and Language Recovery in Bilingual Aphasia: An Active Inference Approach

Understanding the aetiology of the diverse recovery patterns in bilingual aphasia is a theoretical challenge with implications for treatment. Loss of control over intact language networks provides a parsimonious starting point that can be tested using in-silico lesions. We simulated a complex recovery pattern (alternate antagonism and paradoxical translation) to test the hypothesis—from an established hierarchical control model—that loss of control was mediated by constraints on neuromodulatory resources. We used active (Bayesian) inference to simulate a selective loss of sensory precision; i.e., confidence in the causes of sensations. This in-silico lesion altered the precision of beliefs about task relevant states, including appropriate actions, and reproduced exactly the recovery pattern of interest. As sensory precision has been linked to acetylcholine release, these simulations endorse the conjecture that loss of neuromodulatory control can explain this atypical recovery pattern. We discuss the relevance of this finding for other recovery patterns

An Algebraic View of Space/Belief and Extrusion/Utterance for Concurrency/Epistemic Logic

International audienceWe enrich spatial constraint systems with operators to specify information and processes moving from a space to another. We shall refer to these news structures as spatial constraint systems with extrusion. We shall investigate the properties of this new family of constraint systems and illustrate their applications. From a computational point of view the new operators provide for pro-cess/information extrusion, a central concept in formalisms for mobile communication. From an epistemic point of view extrusion corresponds to a notion we shall call utterance; a piece of information that an agent communicates to others but that may be inconsistent with the agent's beliefs. Utterances can then be used to express instances of epistemic notions, which are common place in social media, such as hoaxes or intentional lies. Spatial constraint systems with extrusion can be seen as complete Heyting algebras equipped with maps to account for spatial and epistemic specification

Yet More Modal Logics of Preference Change and Belief Revision

We contrast Bonanno's `Belief Revision in a Temporal Framework' \cite{Bonanno07:briatfTV} with preference change and belief revision from the perspective of dynamic epistemic logic (DEL). For that, we extend the logic of communic

Optimizing Tumor Xenograft Experiments Using Bayesian Linear and Nonlinear Mixed Modelling and Reinforcement Learning

Tumor xenograft experiments are a popular tool of cancer biology research. In a typical such experiment, one implants a set of animals with an aliquot of the human tumor of interest, applies various treatments of interest, and observes the subsequent response. Efficient analysis of the data from these experiments is therefore of utmost importance. This dissertation proposes three methods for optimizing cancer treatment and data analysis in the tumor xenograft context. The first of these is applicable to tumor xenograft experiments in general, and the second two seek to optimize the combination of radiotherapy with immunotherapy in the tumor xenograft context. In tumor xenograft experiments, one commonly observes that growth is exponential (log-linear) initially but later decelerates. For this reason, it is common to model tumor volume using a sigmoid growth curve such as the Gompertz, wherein growth increases in what first appears to be an exponential curve and then decelerates, eventually reaching a plateau. Scientists have advanced multiple biological hypotheses to explain this phenomenon. We propose that a contributing factor in the context of in vivo tumor xenograft studies may be the loss of animals whose tumors are growing most quickly. As they die or require sacrifice, we are left with only the smaller, slower-growing tumors on the remaining animals. To illustrate this point, we show via simulation that the performance of the Gompertz model exceeds that of the exponential when fit to the average of incomplete exponential data where larger tumors are subject to truncation. A log-linear mixed model, however, effectively recovers the individual exponential curves. We conduct an analysis of real tumor xenograft data using these models, which shows that while tumor growth appears Gompertz when analyzing the averages of the available tumor volumes, an exponential mixed model fits well to the individual curves. The efficacy of a radioimmunotherapy regimen for cancer treatment is sensitive to the radiation fractionation scheme. Chapter 2 develops and evaluates a generalized, adaptive method to identify the optimal radiation regimen for use with immunotherapy in the context of a sequential tumor xenograft experiment. We use a predictive model, updated after each new observation, to forecast future tumor growth under each of a set of candidate radioimmunotherapy regimens, selecting the one that yields the best result. We evaluate and compare three versions of our method, characterized by three different predictive models used for forecasting, in a simulation experiment that models an adaptive in vivo tumor xenograft study. We observe that the predictive system characterized by a linear spline mixed model best balances efficiency and robustness and therefore provides the most use in practical applications. We also develop a Reinforcement Learning system to learn and generate such personalized optimal radiotherapy regimens, which is described in Chapter 3. This model was developed based on a set of pre-clinical experimental data and can capture, in the context of combination therapy, the dependence of performance on radiotherapy scheduling. The timings chosen by the agent outperform the fixed application of the best-performing timing observed in an in vivo experiment to all individuals. This preliminary endeavor provides methodological foundation for a future adaptive in vivo tumor xenograft experiment, and potentially a subsequent human trial

AGM 25 years: twenty-five years of research in belief change

The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases. In this review, the first twenty five years of this development are summarized. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, computatibility of AGM operations, and criticism of the model.info:eu-repo/semantics/publishedVersio