We present a formal semantical model to capture action, belief and intention, based on the "database perspective" (Shoham, 2009). We then provide postulates for belief and intention revision, and state a representation theorem relating our postulates to the formal model. Our belief postulates are in the spirit of the AGM theory; the intention postulates stand in rough correspondence with the belief postulates.
Icard et al. introduce a semantics for actions over time, provide an axiomatization for this logic, and use this logic to define coherence conditions for a belief-intention database. First, we show incompleteness of their axiomatization and we adapt their semantics and provide a complete axiomatization for it. Second, we show that Icard et al.'s definition of coherence is too weak, and we define a stronger notion of coherence using our new logic.
IBM has unveiled its first commercial quantum computer at this year's Consumer Electronics Show (CES) in Las Vegas. Named the IBM Q System One (the Q), the 20-quantum bits, or qubits device is the first quantum system built specifically for business use. IBM touts the Q as the "world's first fully integrated universal quantum computing system." This is "a major step forward in the commercialization of quantum computing," Arvind Krishna, IBM's senior VP of hybrid cloud and director of research, said in a statement. The "new system is critical in expanding quantum computing beyond the walls of the research lab as we work to develop practical quantum applications for business and science."
Animal behavior is not driven simply by its current observations, but is strongly influenced by internal states. Estimating the structure of these internal states is crucial for understanding the neural basis of behavior. In principle, internal states can be estimated by inverting behavior models, as in inverse model-based Reinforcement Learning. However, this requires careful parameterization and risks model-mismatch to the animal. Here we take a data-driven approach to infer latent states directly from observations of behavior, using a partially observable switching semi-Markov process. This process has two elements critical for capturing animal behavior: it captures non-exponential distribution of times between observations, and transitions between latent states depend on the animal's actions, features that require more complex non-markovian models to represent. To demonstrate the utility of our approach, we apply it to the observations of a simulated optimal agent performing a foraging task, and find that latent dynamics extracted by the model has correspondences with the belief dynamics of the agent. Finally, we apply our model to identify latent states in the behaviors of monkey performing a foraging task, and find clusters of latent states that identify periods of time consistent with expectant waiting. This data-driven behavioral model will be valuable for inferring latent cognitive states, and thereby for measuring neural representations of those states.
In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the user's "gut feelings" about a situation and suggests what, if any, are to be tested and in what order, and it does so with a user friendly interface.