Axiomatization of the AGM theory of belief revision in a temporal logic

It is natural to think of belief revision as the interaction of belief and information over time. Thus branching-time temporal logic seems a natural setting for a theory of belief revision. We propose two extensions of a modal logic that, besides the ""next-time"" temporal operator, contains a belief operator and an information operator. The first logic is shown to provide an axiomatization of the first six postulates of the AGM theory of belief revision, while the second, stronger, logic provides an axiomatization of the full set of AGM postulates.Belief revision, information, temporal logic, AGM theory

Four Logics for Minimal Belief Revision

It is natural to think of belief revision as the interaction of belief and information over time. Thus branching-time temporal logic seems a natural setting for a theory of belief revision. We propose a logic based on three modal operators: a belief operator, an information operator and a next-time operator. Four logics of increasing strength are proposed. The first is a logic that captures the most basic notion of minimal belief revision. The second characterizes the qualitative content of Bayes' rule. The third provides an axiomatization of the AGM theory of belief revision and the fourth provides a characterization of the notion of plausibility ordering of the set of possible worlds.

Wearing the Inside Out: Using Long Short-Term Memory Networks and Wearable Data to Identify Human Emotions

Studying emotions may sound unusual in computer science, a field based on quantifiable data and rationality. Contrary to belief, studies have shown any decision is highly dependent on emotional input. To improve human-computer interaction, it is crucial to improve our understanding of human emotions and teach machines to identify them. With large amounts of information streaming available from our environment, identifying our current emotional state becomes challenging, even at the individual self-level. This project aims to identify indicative emotional temporal data from wearable devices. Using brain activity data from an EEG and smart watches that record data, such as heart-beat, physical motions and glucose-levels, we hope to find a correlation that will enable us to train a neural Long Short-Term Memory Network (LSTMN) that classifies the temporal physical-state data into the emotional state of the subject. LSTMNs allow the use of previous long- and short-term data points, expanding our understanding of what our body is telling us about our psyche

Wearing the Inside Out: Using Long Short-Term Memory Networks and Wearable Data to Identify Human Emotions

Studying emotions may sound unusual in computer science, a field based on quantifiable data and rationality. Contrary to belief, studies have shown any decision is highly dependent on emotional input. To improve human-computer interaction, it is crucial to improve our understanding of human emotions and teach machines to identify them. With large amounts of information streaming available from our environment, identifying our current emotional state becomes challenging, even at the individual self-level. This project aims to identify indicative emotional temporal data from wearable devices. Using brain activity data from an EEG and smart watches that record data, such as heart-beat, physical motions and glucose-levels, we hope to find a correlation that will enable us to train a neural Long Short-Term Memory Network (LSTMN) that classifies the temporal physical-state data into the emotional state of the subject. LSTMNs allow the use of previous long- and short-term data points, expanding our understanding of what our body is telling us about our psyche

Detectability thresholds and optimal algorithms for community structure in dynamic networks

We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.Comment: 9 pages, 3 figure

Stochastic Prediction of Multi-Agent Interactions from Partial Observations

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read