Byzantine Agreement with Optimal Early Stopping, Optimal Resilience and Polynomial Complexity

We provide the first protocol that solves Byzantine agreement with optimal early stopping ($\min\{f+2,t+1\}$ rounds) and optimal resilience ($n>3t$) using polynomial message size and computation. All previous approaches obtained sub-optimal results and used resolve rules that looked only at the immediate children in the EIG (\emph{Exponential Information Gathering}) tree. At the heart of our solution are new resolve rules that look at multiple layers of the EIG tree.Comment: full version of STOC 2015 abstrac

Lower Bounds on Implementing Robust and Resilient Mediators

We consider games that have (k,t)-robust equilibria when played with a mediator, where an equilibrium is (k,t)-robust if it tolerates deviations by coalitions of size up to k and deviations by up to $t$ players with unknown utilities. We prove lower bounds that match upper bounds on the ability to implement such mediators using cheap talk (that is, just allowing communication among the players). The bounds depend on (a) the relationship between k, t, and n, the total number of players in the system; (b) whether players know the exact utilities of other players; (c) whether there are broadcast channels or just point-to-point channels; (d) whether cryptography is available; and (e) whether the game has a $k+t)-punishment strategy; that is, a strategy that, if used by all but at most$k+t$ players, guarantees that every player gets a worse outcome than they do with the equilibrium strategy

Brief Announcement: Authenticated Consensus in Synchronous Systems with Mixed Faults

Protocols solving authenticated consensus in synchronous networks with Byzantine faults have been widely researched and known to exists if and only if n > 2f for f Byzantine faults. Similarly, protocols solving authenticated consensus in partially synchronous networks are known to exist if n > 3f+2k for f Byzantine faults and k crash faults. In this work we fill a natural gap in our knowledge by presenting MixSync, an authenticated consensus protocol in synchronous networks resilient to f Byzantine faults and k crash faults if n > 2f+k. As a basic building block, we first define and then construct a publicly verifiable crusader agreement protocol with the same resilience. The protocol uses a simple double-send round to guarantee non-equivocation, a technique later used in the MixSync protocol. We then discuss how to construct a state machine replication protocol using these ideas, and how they can be used in general to make such protocols resilient to crash faults. Finally, we prove lower bounds showing that n > 2f+k is optimally resilient for consensus and state machine replication protocols

Colordag: An Incentive-Compatible Blockchain

We present Colordag, a blockchain protocol where following the prescribed strategy is, with high probability, a best response as long as all miners have less than 1/2 of the mining power. We prove the correctness of Colordag even if there is an extremely powerful adversary who knows future actions of the scheduler: specifically, when agents will generate blocks and when messages will arrive. The state-of-the-art protocol, Fruitchain, is an epsilon-Nash equilibrium as long as all miners have less than 1/2 of the mining power. However, there is a simple deviation that guarantees that deviators are never worse off than they would be by following Fruitchain, and can sometimes do better. Thus, agents are motivated to deviate. Colordag implements a solution concept that we call epsilon-sure Nash equilibrium and does not suffer from this problem. Because it is an epsilon-sure Nash equilibrium, Colordag is an epsilon Nash equilibrium and with probability (1 - epsilon) is a best response.Comment: To be published in DISC 202

Tight binding description of the STM image of molecular chains

A tight binding model for scanning tunneling microscopy images of a molecule adsorbed on a metal surface is described. The model is similar in spirit to that used to analyze conduction along molecular wires connecting two metal leads and makes it possible to relate these two measurements and the information that may be gleaned from the corresponding results. In particular, the dependence of molecular conduction properties along and across a molecular chain on the chain length, intersite electronic coupling strength and on thermal and disorder effects are discussed and contrasted. It is noted that structural or chemical defects that may affect drastically the conduction along a molecular chain have a relatively modest influence on conduction across the molecular wire in the transversal direction.Comment: 22 pages, 9 figures, Israel J Chemistry, in pres

QHD: A brain-inspired hyperdimensional reinforcement learning algorithm

Reinforcement Learning (RL) has opened up new opportunities to solve a wide range of complex decision-making tasks. However, modern RL algorithms, e.g., Deep Q-Learning, are based on deep neural networks, putting high computational costs when running on edge devices. In this paper, we propose QHD, a Hyperdimensional Reinforcement Learning, that mimics brain properties toward robust and real-time learning. QHD relies on a lightweight brain-inspired model to learn an optimal policy in an unknown environment. We first develop a novel mathematical foundation and encoding module that maps state-action space into high-dimensional space. We accordingly develop a hyperdimensional regression model to approximate the Q-value function. The QHD-powered agent makes decisions by comparing Q-values of each possible action. We evaluate the effect of the different RL training batch sizes and local memory capacity on the QHD quality of learning. Our QHD is also capable of online learning with tiny local memory capacity, which can be as small as the training batch size. QHD provides real-time learning by further decreasing the memory capacity and the batch size. This makes QHD suitable for highly-efficient reinforcement learning in the edge environment, where it is crucial to support online and real-time learning. Our solution also supports a small experience replay batch size that provides 12.3 times speedup compared to DQN while ensuring minimal quality loss. Our evaluation shows QHD capability for real-time learning, providing 34.6 times speedup and significantly better quality of learning than state-of-the-art deep RL algorithms