Decentralized Probabilistic World Modeling with Cooperative Sensing

Drawing on the projected increase in computing power, solid-state storage and network communication capacity to be available on personal mobile devices, we propose to build and maintain without prior knowledge a fully distributed decentralized large-scale model of the physical world around us using probabilistic methods. We envisage that, by using the multimodal sensing capabilities of modern personal devices, such a probabilistic world model can be constructed as a collaborative effort of a community of participants, where the model data is redundantly stored on individual devices and updated and refined through short-range wireless peer-to-peer communication. Every device holds model data describing its current surroundings, and obtains model data from others when moving into unknown territory. The model represents common spatio-temporal patterns as observed by multiple participants, so that rogue participants can not easily insert false data and only patterns of general applicability dominate. This paper aims to describe – at a conceptual level – an approach for building such a distributed world model. As one possible world modeling approach, it discusses compositional hierarchies, to fuse the data from multiple sensors available on mobile devices in a bottom-up way. Furthermore, it focuses on the intertwining between building a decentralized cooperative world model and the opportunistic communication between participants

MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder

Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering problem. However, we observe that when the e-graph is not saturated, it cannot represent all possible rewrite opportunities and therefore the phase-ordering problem is re-introduced during the construction phase of the e-graph. To address this problem, we propose MCTS-GEB, a domain-general rewrite system that applies reinforcement learning (RL) to e-graph construction. At its core, MCTS-GEB uses a Monte Carlo Tree Search (MCTS) [3] to efficiently plan for the optimal e-graph construction, and therefore it can effectively eliminate the phase-ordering problem at the construction phase and achieve better performance within a reasonable time. Evaluation in two different domains shows MCTS-GEB can outperform the state-of-the-art rewrite systems by up to 49x, while the optimisation can generally take less than an hour, indicating MCTS-GEB is a promising building block for the future generation of rewrite systems

Rhythm and Randomness in Human Contact

There is substantial interest in the effect of human mobility patterns on opportunistic communications. Inspired by recent work revisiting some of the early evidence for a L\'evy flight foraging strategy in animals, we analyse datasets on human contact from real world traces. By analysing the distribution of inter-contact times on different time scales and using different graphical forms, we find not only the highly skewed distributions of waiting times highlighted in previous studies but also clear circadian rhythm. The relative visibility of these two components depends strongly on which graphical form is adopted and the range of time scales. We use a simple model to reconstruct the observed behaviour and discuss the implications of this for forwarding efficiency