A state-feedback controller was designed to simulate the Starling response of the heart in a mock circulatory system (MCS). The controller drives a voice coil actuator (VCA) to follow a reference volume, and thus generate the desired chamber pressure, by using position and speed feedbacks. The reference volume was adjusted according to the maximum ventricular elastance (E max ), end-systolic ventricular pressure, and left atrial pressure to respond to load variation in the MCS. The controller was tested in computer simulation by changing the load conditions and E max of the MCS. The MCS along with the controller was able to reproduce human heart function from healthy to sick conditions. A rotary ventricular assist device (VAD) was then introduced in the simulation to test the MCS. The MCS was able to produce a consistent cardiac function even with the presence of the VAD. These results suggest that the new MCS control system is able to simulate the cardiac function for VAD test.
This paper describes WORD2HTN, an algorithm for learning hierarchical tasks and goals from plan traces in planning domains. WORD2HTN combines semantic text analysis techniques and subgoal learning in order to generate Hierarchical Task Networks (HTNs). Unlike existing HTN learning algorithms, WORD2HTN learns distributed vector representations that represent the similarities and semantics of the components of plan traces. WORD2HTN uses those representations to cluster them into task and goal hierarchies, which can then be used for automated planning. We describe our algorithm and present our preliminary evaluation thereby demonstrating the promise of WORD2HTN.
Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a positive (collaborative) or negative (adversarial) way. Goal recognition has been successfully used in many domains, but it has been seldom been used by financial institutions. We claim the techniques are ripe for its wide use in finance-related tasks. The main two approaches to perform goal recognition are model-based (planningbased) and model-free (learning-based). In this paper, we adapt state-ofthe-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains. We analyze the experimental data to understand the trade-offs of using both types of methods. The experiments show that planning-based approaches are ready for some goal-recognition finance tasks.
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