Synthesis of surveillance strategies via belief abstraction

We provide a novel framework for the synthesis of a controller for a robot with a surveillance objective, that is, the robot is required to maintain knowledge of the location of a moving, possibly adversarial target. We formulate this problem as a one-sided partial-information game in which the winning condition for the agent is specified as a temporal logic formula. The specification formalizes the surveillance requirement given by the user by quantifying and reasoning over the agent's beliefs about a target's location. We also incorporate additional non-surveillance tasks. In order to synthesize a surveillance strategy that meets the specification, we transform the partial-information game into a perfect-information one, using abstraction to mitigate the exponential blow-up typically incurred by such transformations. This transformation enables the use of off-the-shelf tools for reactive synthesis. We evaluate the proposed method on two case-studies, demonstrating its applicability to diverse surveillance requirements

Synthesis of minimum-cost shields for multi-agent systems

In this paper, we propose a general approach to derive runtime enforcement implementations for multiagent systems, called shields, from temporal logical specifications. Each agent of the multi-agent system is monitored, and if needed corrected, by the shield, such that a global specification is always satisfied. The different ways of how a shield can interfere with each agent in the system in case of an error introduces the need for quantitative objectives. This work is the first to discuss the shield synthesis problem with quantitative objectives. We provide several cost functions that are utilized in the multi-agent setting and provide methods for the synthesis of cost-optimal shields and fair shields, under the given assumptions on the multi-agent system. We demonstrate the applicability of our approach via a detailed case study on UAV mission planning for warehouse logistics and simulating the shielded multi-agent system on ROS/Gazebo

A potential therapeutic role in multiple sclerosis for stigmast-5,22-dien-3 beta-ol myristate isolated from Capparis ovata

Multiple sclerosis (MS) is an autoimmune disease of the human central nervous system. It is one of the most common neurological disorders around the world and there is still no complete cure for MS. Purification of a terpenoid from Capparis ovata was carried out and its structure was elucidated as stigmast-5,22-dien-3 beta-ol, myristate (3 beta, 22E-stigmasteryl myristate; SDM) by NMR and mass spectral analyses. No information regarding its any health effect is available in the literature. In the present study, we have described its effects on inflammatory factors such as the expression levels of cytokines, chemokines and adhesion molecules as well as apoptosis/infiltration and myelination in SH-SY5Y cells. The expression levels of proinflammatory or inflammatory cytokines and chemokines such as NF-.B1, CCL5, CXCL9, CXCL10 and HIF1A along with T-cell activating cytokines such as IL-6 and TGFB1 were significantly downregulated with SDM treatment. Moreover, the expression levels of the main myelin proteins such as MBP, MAG and PLP that are essential for healthy myelin architecture were significantly up-regulated. The results presented in this study strongly suggest that the SDM offers a unique possibility to be used with autoimmune diseases, including MS due to its activity on the manipulation of cytokines and the promotion of myelin formation

Learning Interpretable Temporal Properties from Positive Examples Only

We consider the problem of explaining the temporal behavior of black-boxsystems using human-interpretable models. To this end, based on recent researchtrends, we rely on the fundamental yet interpretable models of deterministicfinite automata (DFAs) and linear temporal logic (LTL) formulas. In contrast tomost existing works for learning DFAs and LTL formulas, we rely on onlypositive examples. Our motivation is that negative examples are generallydifficult to observe, in particular, from black-box systems. To learnmeaningful models from positive examples only, we design algorithms that relyon conciseness and language minimality of models as regularizers. To this end,our algorithms adopt two approaches: a symbolic and a counterexample-guidedone. While the symbolic approach exploits an efficient encoding of languageminimality as a constraint satisfaction problem, the counterexample-guided onerelies on generating suitable negative examples to prune the search. Both theapproaches provide us with effective algorithms with theoretical guarantees onthe learned models. To assess the effectiveness of our algorithms, we evaluateall of them on synthetic data.<br

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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