From BDI and stit to bdi-stit logic

Since it is desirable to be able to talk about rational agents forming attitudes toward their concrete agency, we suggest an introduction of doxastic, volitional, and intentional modalities into the multi-agent logic of deliberatively seeing to it that, dstit logic. These modalities are borrowed from the well-known BDI (belief-desire-intention) logic. We change the semantics of the belief and desire operators from a relational one to a monotonic neighbourhood semantic in order to handle ascriptions of conflicting but not inconsistent beliefs and desires as being satisfiable. The proposed bdi-stit logic is defined with respect to branching time frames, and it is shown that this logic is a generalization of a bdi logic based on branching time possible worlds frames (but without temporal operators) and dstit logic. The new bdi-stit logic generalizes bdi and dstit logic in the sense that for any model of bdi or dstit logic, there is an equivalent bdi-stit model

Airborne lipid antigens mobilize resident intravascular NKT cells to induce allergic airway inflammation

Resident intravascular NKT cells exacerbate airway hyperreactivity in mice dependent on dendritic cell co-presentation of glycolipid and peptide antigens

STIT is dangerously undecidable

International audienceSTIT is a potential logical framework to capture responsibility , counterfactual emotions and norms, which are main ingredients for specifying behaviors of virtual agents. We identify here a new fragment and its satisfiability problem is NP-complete and in Σ 3 when the number of agents is unbounded. We also identify a slightly more expressive fragment which is undecidable

Atmosphere and Ocean Monitoring using GNSS reflected signals: Current Status and Prospects at GFZ

Deployment of reflected signals of Global Navigation Satellite System (GNSS) from the Earth's surface, so-called GNSS-Reflectometry (GNSS-R), has emerged as a powerful tool in obtaining a variety of geophysical parameters and surface properties. This technique can meet the growing demand for high spatiotemporal resolution geophysical data and close the data gaps due to its advantageous characteristics: it is a multistatic radar method using heretofore existing signals from numerous GNSS satellites as the transmitters. The low-costs, low-mass, and low-power recievers can be implemented in the ground-based stations or different air/space-borne platforms, such as Cyclone GNSS (CYGNSS), a constellation of eight small Low Earth Orbiting (LEO), launched in December 2016. Here, we present the derived GNSS-R surface wind speeds, validations, and comparisons to conventional remote sensing instruments. The robustness of the derived wind speeds during rain events over oceans is demonstrated being a piece of evidence for the suitability of the technique for severe weather monitoring. Using modern data scientific techniques, further accuracy of the wind speed data is sought. In different experiments on the implementation of Artificial Intelligence techniques, it is shown how such methods could lead to a higher quality of GNSS-R data products. Potentials are still being explored and new ideas are being created leading to novel GNSS-R applications. It is shown that the technique can detect precipitation over oceans. In addition to empirical evidence, physical feasibility and schemes are discussed. Besides, it is shown how the technique can also respond to ocean mesoscale eddies initiating future studies on this application