In this paper, we examine the modularity assumption of behaviour-based models: that complex functionalities can be achieved by decomposition into simpler behaviours. In particular we look at the issue of conflicts among robot behaviour modules. The chief contribution of this work is a formal characterization of temporal cycles in behaviour systems and the development of an algorithm for detecting and avoiding such conflicts. We develop the mechanisms of stimulus specialization and response generalization for eliminating conflicts. The probable conflicts can be detected and eliminated before implementation. However the process of cycle elimination weakens the behaviour structure. We show how (a) removing conflicts results in less flexible and less useful behaviour modules and (b) the probability of conflict is greater for more powerful behaviour systems.
Panda Behaviour is a script-based Behaviour Tree engine for Unity which allows to easily define complex, scalable and reusable logic for your game. Panda Behaviour is delivered as a single GameObject component. This component can be attached to any GameObject to model its behaviour by a combination of C# and BT scripts, which are scripts written in a minimalist built-in language. The states of behaviour tree are visualized at run-time within the Inspector providing detailed information about the behaviour in realtime, which is valuable for debugging.
In many medical settings precise numerical data for all findings of interest either is unavailable or is too timeconsuming to measure. In such situations where a mixture of exact and approximate data is available, a model which can reason with such data and produce output of precision proportional to the the precision of the available findings is desirable. We are developing an acute cardiovascular model which can reason with mixed qualitative and quantitative data, the principles of which can be applied to any mixed-data situation. Our objective is to construct a computationally efficient, first-principles model of the cardiovascular system's response to blood loss and fluid replacement. Such a model can be used to estimate the volume of blood loss within therapeutically acceptable ranges and predict the future clinically-relevant cardiovascular states of the patient, i.e. to predict initial blood transfusion requirements within -t-1 unit (500cc), and to predict when the patient may go beyond Class II shock (1500cc blood loss or 30% blood loss) either in the absence of remedial procedures or in the presence of fluid replacement or other therapeutic actions.
SAT solvers have become efficient for solving NP-complete problems (and beyond). Usually, those problems are solved by direct translation to SAT or by solving iteratively SAT problems in a procedure like CEGAR. Recently, a new recursive CEGAR loop working with two abstraction levels, called RECAR, was proposed and instantiated for modal logic K. We aim to complete this work for modal logics based on axioms (B), (D), (T), (4) and (5. Experimental results show that the approach is competitive against state-of-the-art solvers for modal logics K, KT, and S4.
In this study, we propose a machine-learning-based approach to identify the modal parameters of the output only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach s good capability in blindly extracting modal information from system responses.