Publications by Boi Faltings | EPFL Graph Search

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In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In th ...

We present a new hybrid algorithm for local search in distributed combinatorial optimization. This method is a mix between classical local search methods in which nodes take decisions based only on local information, and full inference methods that guarantee completeness. In general, classical inference methods are time and space exponential in a parameter of the graph called the induced width, thus they may be infeasible for dense problems. On the other hand, local search methods suffer from the fact that

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distr ...

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of rid ...

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a truste ...

Users often have to search for a most preferred item but do not know how to state their preferences in the language allowed by the system. Example-Critiquing has been proposed as a mixed-initiative technique for allowing them to construct their preference ...

Building tight and conservative enclosures of the solution set is of crucial importance in the design of efficient complete solvers for numerical constraint satisfaction problems (NCSPs). This paper proposes a novel generic algorithm enabling the cooperati ...

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (e.g., crowdsensing). More sophisticated applications, like cryptocurrencies, need ...

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

Personalization in e-commerce has so far been server-centric, requiring users to create a separate individual profile on each server that they like to access. As product information is increasingly coming from multiple and heterogeneous sources, the number of profiles becomes unmanageably large. We present SmartClient, a technology based on constraint programming where a thin but intelligent client provides personalized information access for its user. As the process can run on the user's side, it allows mu

Several peer consistency mechanisms have been proposed to incentivize agents for honestly solving crowdsourcing tasks. These game-theoretic mechanisms evaluate the answers provided by an agent based on the correlation with answers provided by other agents ...

The increasing sophistication of text-to-image generative models has led to complex challenges in defining and enforcing copyright infringement criteria and protection. Existing methods, such as watermarking and dataset deduplication, fail to provide comprehensive solutions due to the lack of standardized metrics and the inherent complexity of addressing copyright infringement in diffusion models. To deal with these challenges, we propose a Reinforcement Learning-based Copyright Protection (RLCP) method for

Inherent limitations of contemporary machine learning systems in crucial areas—importantly in continual learning, information reuse, comprehensibility, and integration with deliberate behavior—are receiving increasing attention. To address these challenges, we introduce a system design, fueled by a novel learning approach conceptually grounded in principles of evolutionary developmental biology, that overcomes key limitations of current methods. Our design comprises three core components: The Modeller, a gr

We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing or opinion polls. To encourage participation and avoid selection bias, agents should be rewarded for the information they provide. It is important the the rewards provide incentives for relevant and truthful information and discourage random or malicious reports. Incentive schemes can be based on the fact that an agent's private inform

The internet presents people with an increasingly bewildering variety of choices. Online consumers have to rely on computerized search tools to find the most preferred option in a reasonable amount of time. Recommender systems address this problem by searc ...

Network administrators are faced with a large amount of network data that they need to sift through to analyze user behaviors and detect anomalies. Through a network monitoring tool, we obtained TCP and UDP connection records together with additional infor ...

In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who shou ...

Communication networks are expected to offer a wide range of services to an increasingly large number of users, with a diverse range of quality of service. This calls for efficient control and management of these networks. In this paper, we address the problem of quality-of-service routing, more specifically the planning of bandwidth allocation to communication demands. Shortest path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even con

This paper presents a multiagent-system for integrating AL-ICE: a real-life, distributed, large-scale particle physics experiment of the LHC project at CERN. The work is concerned with solving the large-scale coordination problem using a distributed constraint satisfaction algorithm. We believe that until now, no such algorithm has successfully handled problems of this size. The developed algorithm is a local search algorithm, based on the distributed breakout algorithm and is extended by an incremental pro

Boi Faltings

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M-DPOP: Faithful distributed implementation of efficient social choice problems

A Propagation/Local Search Hybrid for Distributed Optimization

Toward Mobile Distributed Ledgers

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

Poster: LocalCoin: An Ad-hoc Payment Scheme for Areas with High Connectivity

Stimulating preference expression using suggestions

Enhancing numerical constraint propagation using multiple inclusion representations

Mneme: A Mobile Distributed Ledger

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

Improved Cooperation by Exploiting a Common Signal

Personalized navigation of heterogeneous product spaces using SmartClient

Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data

RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model

Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility

Eliciting Truthful Information with the Peer Truth Serum

Increasing user decision accuracy using suggestions

Relational network-service clustering analysis with set evidences

Distributed Constraint Optimization under Stochastic Uncertainty

IconoNET: a tool for automated bandwidth allocation planning

A Multi-Agent System for Integrating a Large-Scale Project