Abstract. Recent work has shown that Dynamic Epistemic Logic (DEL) offers a solid foundation for automated planning under partial observability and non-determinism. Under such circumstances, a plan must branch if it is to guarantee achieving the goal under all contingencies (strong planning). Without branching, plans can offer only the possibility of achieving the goal (weak planning). We show how to formulate planning in uncertain domains using DEL and give a language of conditional plans. Translating this language to standard DEL gives verification of both strong and weak plans via model checking. In addition to plan verification, we provide a tableau-inspired algorithm for synthesising plans, and show this algorithm to be terminating, sound and complete.

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Plausibility models are Kripke models that agents use to reason about knowledge and belief, both of themselves and of each other. Such models are used to interpret the notions of conditional belief, degrees of belief, and safe belief. The logic of conditional belief contains that modality and also the knowledge modality, and similarly for the logic of degrees of belief and the logic of safe belief. With respect to these logics, plausibility models may contain too much information. A proper notion of bisimulation is required that characterises them. We define that notion of bisimulation and prove the required characterisations: on the class of image-finite and preimagefinite models (with respect to the plausibility relation), two pointed Kripke models are modally equivalent in either of the three logics, if and only if they are bisimilar. As a result, the information content of such a model can be similarly expressed in the logic of conditional belief, or the logic of degrees of belief, or that of safe belief. This, we found a surprising result. Still, that does not mean that the logics are equally expressive: the logics of conditional and degrees of belief are incomparable, the logics of degrees of belief and safe belief are incomparable, while the logic of safe belief is more expressive than the logic of conditional belief. In view of the result on bisimulation B Thomas Bolander 123 Synthese characterisation, this is an equally surprising result. We hope our insights may contribute to the growing community of formal epistemology and on the relation between qualitative and quantitative modelling.

Seismocardiography (SCG) is a non-invasive method for measurement of vibrations on the chest wall originating from the heart. Respiration changes the morphology of the SCG-signal and analyzing these changes could improve the diagnostic value of SCG. This study aimed to determine the nasal respiration signal amplitude at mitral closure (MC) and aortic opening (AO) using SCG features. The three proposed methods for this were multiple regression analysis (MRA), support vector regression (SVR), and a neural network (NN). SCG, Electrocardiography and nasal-catheter flow signals were acquired from 18 healthy subjects (age 29 ± 6). SCG-signal fiducial points were used as features and were found using an automatic algorithm followed by manual verification. Fiducial points amplitudes, timings between these and frequency components formed 12 features. All models were trained on 80% of the data, underwent 10-fold cross-validation and were tested on the remaining 20% of the data. Predictions on test data for MC and AO time points, the Pearson correlations coefficient, and sum of squared errors of prediction were: