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Tommaso Bianchi

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3 papers

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IJCAI Conference 2024 Conference Paper

Enhancing Manufacturing with AI-powered Process Design

Gianmarco Genalti
Gabriele Corbo
Tommaso Bianchi
Marco Missaglia
Luca Negri
Andrea Sala
Luca Magri
Giacomo Boracchi

Manufacturing companies are experiencing a transformative journey, moving from labor-intensive processes to integrating cutting-edge technologies such as digitalization and AI. In this demo paper, we present a novel AI tool to enhance manufacturing processes. Remarkably, our work has been developed in collaboration with Agrati S. p. A. , a worldwide leading company in the bolts manufacturing sector. In particular, we propose an AI-powered tool to address the problem of automatically generating the production cycle of a bolt. Currently, this decision-making task is performed by process engineers who spend several days to study, draw, and test multiple alternatives before finding the desired production cycle. We cast this task as a model-based planning problem, mapping bolt technical drawings and metal deformations to, potentially continuous, states and actions, respectively. Furthermore, we resort to computer vision tools and visual transformers to design efficient heuristics that make the search affordable in concrete applications. Agrati S. p. A. 's process engineers extensively validated our tool, and they are currently using it to support their work. To the best of our knowledge, ours is the first AI tool dealing with production cycle design in bolt manufacturing.

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AAAI Conference 2020 Conference Paper

Coarse Correlation in Extensive-Form Games

Gabriele Farina
Tommaso Bianchi
Tuomas Sandholm

Coarse correlation models strategic interactions of rational agents complemented by a correlation device which is a mediator that can recommend behavior but not enforce it. Despite being a classical concept in the theory of normal-form games since 1978, not much is known about the merits of coarse correlation in extensive-form settings. In this paper, we consider two instantiations of the idea of coarse correlation in extensive-form games: normal-form coarse-correlated equilibrium (NFCCE), already deﬁned in the literature, and extensive-form coarse-correlated equilibrium (EFCCE), a new solution concept that we introduce. We show that EFC- CEs are a subset of NFCCEs and a superset of the related extensive-form correlated equilibria. We also show that, in n-player extensive-form games, social-welfare-maximizing EFCCEs and NFCCEs are bilinear saddle points, and give new efﬁcient algorithms for the special case of two-player games with no chance moves. Experimentally, our proposed algorithm for NFCCE is two to four orders of magnitude faster than the prior state of the art.

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NeurIPS Conference 2019 Conference Paper

Learning to Correlate in Multi-Player General-Sum Sequential Games

Andrea Celli
Alberto Marchesi
Tommaso Bianchi
Nicola Gatti

In the context of multi-player, general-sum games, there is a growing interest in solution concepts involving some form of communication among players, since they can lead to socially better outcomes with respect to Nash equilibria and may be reached through learning dynamics in a decentralized fashion. In this paper, we focus on coarse correlated equilibria (CCEs) in sequential games. First, we complete the picture on the complexity of finding social-welfare-maximizing CCEs by proving that the problem is not in Poly-APX, unless P = NP, in games with three or more players (including chance). Then, we provide simple arguments showing that CFR---working with behavioral strategies---may not converge to a CCE in multi-player, general-sum sequential games. In order to amend this issue, we devise two variants of CFR that provably converge to a CCE. The first one (CFR-S) is a simple stochastic adaptation of CFR which employs sampling to build a correlated strategy, whereas the second variant (called CFR-Jr) enhances CFR with a more involved reconstruction procedure to recover correlated strategies from behavioral ones. Experiments on a rich testbed of multi-player, general-sum sequential games show that both CFR-S and CFR-Jr are dramatically faster than the state-of-the-art algorithms to compute CCEs, with CFR-Jr being also a good heuristic to find socially-optimal CCEs.

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