Distributed ledger technology benefits society by enabling an ecosystem of decentralised finance. However the pseudo-anonymised nature of transactions has also been an enabler of new routes for illicit activities ranging from individual scams to organised crimes. Current solutions for identifying addresses involved in illicit activities (illicit addresses) rely on commercial intelligence services, which are costly due to the intensive investigative efforts required. We propose Ledgit, an automatic real-time service for diagnosing illicit addresses on the Bitcoin blockchain. Ledgit is based solely on publicly available data, and uses an unsupervised clustering method that combines information from textual reports and the blockchain graph to assign a risk score that a Bitcoin address is involved in illicit activities. We verify the system with labeled addresses, showing high performance in identifying illicit addresses. Finally, we provide an intuitive user interface that provides accessible risk assessment with graph and report analytics.

Abstract Reinforcement learning(RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary RL research is to discover how to learn with less data. Previous work has shown that domain information can be successfully used to shape the reward; by adding additional reward information, the agent can learn with much less data. Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered. While suchpotential-based reward shaping(PBRS) holds promise, it is limited by the need for a well-defined potential function. Ideally, we would like to be able to take arbitrary advice from a human or other agent and improve performance without affecting the optimal policy. The recently introduceddynamic potential-based advice(DPBA) was proposed to tackle this challenge by predicting the potential function values as part of the learning process. However, this article demonstrates theoretically and empirically that, while DPBA can facilitate learning with good advice, it does in fact alter the optimal policy. We further show that when adding the correction term to “fix” DPBA it no longer shows effective shaping with good advice. We then present a simple method calledpolicy invariant explicit shaping(PIES) and show theoretically and empirically that PIES can use arbitrary advice, speed-up learning, and leave the optimal policy unchanged.

A key challenge in the design of multi-sensor systems is the efficient allocation of scarce resources such as bandwidth, CPU cycles, and energy, leading to the dynamic sensor selection problem in which a subset of the available sensors must be selected at each timestep. While partially observable Markov decision processes (POMDPs) provide a natural decision-theoretic model for this problem, the computational cost of POMDP planning grows exponentially in the number of sensors, making it feasible only for small problems. We propose a new POMDP planning method that uses greedy maximization to greatly improve scalability in the number of sensors. We show that, under certain conditions, the value function of a dynamic sensor selection POMDP is submodular and use this result to bound the error introduced by performing greedy maximization. Experimental results on a real-world dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing methods but incurs only a fraction of the computational cost, leading to much better scalability in the number of cameras.