Herod and Pilate: Two Tableau Provers for Basic Hybrid Logic

International audienceThis work presents two provers for basic hybrid logic HL(@), which have been implemented with the aim of comparing the internalised tableau calculi independently proposed, respectively, by Bolander and Blackburn [3] and Cerrito and Cialdea Mayer [5]. Experimental results are reported, evaluating, from the practical point of view, the different treatment of nominal equalities of the two calculi

Near-Field Pressure Signature Splicing for Low-Fidelity Design Space Exploration of Supersonic Aircraft

As interest in supersonic overland flight intensifies, new ways to meet government restrictions on sonic boom loudness must be implemented. Low-fidelity aerodynamic tools, such as PANAIR, can estimate the near-field pressure signature that ultimately determines the loudness of the sonic boom at the ground. These tools can greatly benefit the exploration of large design spaces due to their computational efficiency. One of the limitations of low-fidelity tools is the accuracy of the solution produced, which is dependent on the fundamental physical assumptions made in the development of the governing equations. If flow patterns are produced that severely violate these fundamental assumptions, the validity of the near-field pressure signature is compromised. A method is proposed that splices together near-field pressure signatures from a low-fidelity and a higher-fidelity tool by cutting each pressure signature at a critical point and then blending the low-fidelity signature into the higher-fidelity signature. By splicing the signatures together, sections of the low-fidelity signature that represent fundamental violations of the governing equation are removed. This method allows for the exploration of the design space corresponding to areas on the geometry that produce accurate results in a low-fidelity signature. The method is tested on the JAXA Wing Body geometry from the Second AIAA Sonic Boom Prediction Workshop and shows that perturbations to this geometry can produce loudness results that match the high-fidelity results to within 0.4 PLdB

Automated Synthesis of Tableau Calculi

This paper presents a method for synthesising sound and complete tableau calculi. Given a specification of the formal semantics of a logic, the method generates a set of tableau inference rules that can then be used to reason within the logic. The method guarantees that the generated rules form a calculus which is sound and constructively complete. If the logic can be shown to admit finite filtration with respect to a well-defined first-order semantics then adding a general blocking mechanism provides a terminating tableau calculus. The process of generating tableau rules can be completely automated and produces, together with the blocking mechanism, an automated procedure for generating tableau decision procedures. For illustration we show the workability of the approach for a description logic with transitive roles and propositional intuitionistic logic.Comment: 32 page

A Multi-Fidelity Prediction of Aerodynamic and Sonic Boom Characteristics of the JAXA Wing Body

This paper presents a detailed comparison between the linear panel solver PANAIR A502 and the in-house Navier–Stokes solver UNS3D for a supersonic low-boom geometry. The high-fidelity flow solver was used to predict both the inviscid and laminar flow about the aircraft geometry. The JAXA wing body was selected as the supersonic low-boom geometry for this study. A comparison of the undertrack near-field pressure signatures showed good agreement between the three levels of model fidelity along the first 0.8L of the signature. Large oscillations in the PANAIR results were observed. The PANAIR discrepancies were traced back to violations of the underlying assumptions within PANAIR: (1) small perturbation velocities and (2) no regions of transonic flow. These violations were due to large changes in surface curvature resulting in a strong expansion wave. While investigating the PANAIR discrepancy, measures of the fundamental assumptions of the Prandtl-Glauert equation used by PANAIR were quantified and used to assess the applicability of PANAIR to a given problem. Further comparison of surface temperatures predicted between the inviscid and laminar solutions was made. It was found that the recovery temperatures predicted by the inviscid solution were 5% less than those predicted by the laminar solution in likely candidate regions for distributed adaptivity. A surface deformation was added to the forward portion of the geometry to asses the viability of a future optimization study in this region. In this study, it was found that the near-field and ground signatures predicted by PANAIR and the UNS3D solutions responded in similar manners to the deformation

Modal satisfiability via SMT solving

Modal logics extend classical propositional logic, and they are robustly decidable. Whereas most existing decision procedures for modal logics are based on tableau constructions, we propose a framework for obtaining decision procedures by adding instantiation rules to standard SAT and SMT solvers. Soundness, completeness, and termination of the procedures can be proved in a uniform and elementary way for the basic modal logic and some extensions.Fil: Areces, Carlos Eduardo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Areces, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Fontaine, Pascal. Université de Lorraine; Francia.Fil: Fontaine, Pascal. National Institute for Research in Digital Science and Technology; Francia.Fil: Merz, Stephan. Université de Lorraine; Francia.Fil: Merz, Stephan. National Institute for Research in Digital Science and Technology; Francia.Ciencias de la Computació

Hormonal control of p53 and chemoprevention

Improvements in the detection and treatment of breast cancer have dramatically altered its clinical course and outcome. However, prevention of breast cancer remains an elusive goal. Parity, age of menarche, and age at menopause are major risk factors drawing attention to the important role of the endocrine system in determining the risk of breast cancer, while heritable breast cancer susceptibility syndromes have implicated tumor suppressor genes as important targets. Recent work demonstrating hormonal modulation of the p53 tumor suppressor pathway draws together these established determinants of risk to provide a model of developmental susceptibility to breast cancer. In this model, the mammary epithelium is rendered susceptible due to impaired p53 activity during specific periods of mammary gland development, but specific endocrine stimuli serve to activate p53 function and to mitigate this risk. The results focus attention on p53 as a molecular target for therapies to reduce the risk of breast cancer

Algorithmic iteration for computational intelligence

Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation. A more concrete object of theoretical analysis is algorithmic iteration for computational intelligence, intended as the theoretical and practical ability of algorithms to design other algorithms for actions aimed at solving well-specified tasks. We know this ability is already shown by current AIs, and understanding its limits is an essential step in qualifying claims about machine awareness and Super-AI. We propose a formal translation of algorithmic iteration in a fragment of modal logic, formulate principles of transparency and faithfulness across human and machine intelligence, and consider the relevance to theoretical research on (Super)-AI as well as the practical import of our results

Expressing Belief Flow in Assertion Networks

Abstract. In the line of some earlier work done on belief dynamics, we propose an abstract model of belief propagation on a graph based on the methodology of the revision theory of truth. A modal language is developed for portraying the behavior of this model, and its expressiveness is discussed. We compare the proposal of this model as well as the language developed with some of the existing frameworks for modelling communication situations.

Logic, Probability and Action: A Situation Calculus Perspective

The unification of logic and probability is a long-standing concern in AI, and more generally, in the philosophy of science. In essence, logic provides an easy way to specify properties that must hold in every possible world, and probability allows us to further quantify the weight and ratio of the worlds that must satisfy a property. To that end, numerous developments have been undertaken, culminating in proposals such as probabilistic relational models. While this progress has been notable, a general-purpose first-order knowledge representation language to reason about probabilities and dynamics, including in continuous settings, is still to emerge. In this paper, we survey recent results pertaining to the integration of logic, probability and actions in the situation calculus, which is arguably one of the oldest and most well-known formalisms. We then explore reduction theorems and programming interfaces for the language. These results are motivated in the context of cognitive robotics (as envisioned by Reiter and his colleagues) for the sake of concreteness. Overall, the advantage of proving results for such a general language is that it becomes possible to adapt them to any special-purpose fragment, including but not limited to popular probabilistic relational models