After 20 years of testing a framework for affective user responses to artificial agents and robots, we compiled a full formalization of our findings so to make the agent respond affectively to its user. Silicon Coppélia as we dubbed our system works from the features of the observed other, appraises these in various domains (e.g., ethics and affordances), then compares them to goals and concerns of the agent, to finally reach a response that includes intentions to work with the user as well as a level of being engaged with the user. This ultimately results in an action that adds to or changes the situation both agencies are in. Unlike many other systems, Silicon Coppélia can deal with ambiguous emotions of its user and has ambiguous 'feelings' of its own, which makes its decisions quite human-like. In the current paper, we advance a fuzzy-sets approach and show the inner workings of our system through an elaborate example. We present a number of simulation experiments, one of which showed decision behaviors based on biases when agent goals had low priorities. Silicon Coppélia is open to scrutiny and experimentation by way of an open-source implementation in Ptolemy.

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In the paper the Bayesian and the least squares methods of quantum state tomography are compared for a single qubit. The quality of the estimates are compared by computer simulation when the true state is either mixed or pure. The fidelity and the Hilbert-Schmidt distance are used to quantify the error.It was found that in the regime of low measurement number the Bayesian method outperforms the least squares estimation. Both methods are quite sensitive to the degree of mixedness of the state to be estimated, that is, their performance can be quite bad near pure states.