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Andrea Passarella

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers
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7

AIJ Journal 2025 Journal Article

Human-AI coevolution

  • Dino Pedreschi
  • Luca Pappalardo
  • Emanuele Ferragina
  • Ricardo Baeza-Yates
  • Albert-László Barabási
  • Frank Dignum
  • Virginia Dignum
  • Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

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IJCAI Conference 2025 Conference Paper

Human-AI Coevolution (Abstract Reprint)

  • Dino Pedreschi
  • Luca Pappalardo
  • Emanuele Ferragina
  • Ricardo Baeza-Yates
  • Albert-László Barabási
  • Frank Dignum
  • Virginia Dignum
  • Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i. e. , scientific, legal and socio-political.

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TAAS Journal 2024 Journal Article

Optimizing Delegation in Collaborative Human-AI Hybrid Teams

  • Andrew Fuchs
  • Andrea Passarella
  • Marco Conti

When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. From our review of current state of the art, we present a novel manager model for oversight of hybrid teams by our support for diverse agents and decision-maker operations across multiple time steps and decisions. In our model, we restrict the manager’s task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore, our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager’s performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact on our manager, with some cases resulting in increased team performance up to \(\approx 187\%\) that of the best solo agent performance.

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TAAS Journal 2023 Journal Article

Modeling, Replicating, and Predicting Human Behavior: A Survey

  • Andrew Fuchs
  • Andrea Passarella
  • Marco Conti

Given the popular presupposition of human reasoning as the standard for learning and decision making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, and Machine Learning, among others, integrate concepts that are assumed components of human reasoning. These serve as techniques to replicate and understand the behaviors of humans. In addition, next-generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, allowing them not only to replicate human models as a technique to “learn” but also to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this article is to provide a succinct yet systematic review of important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques that learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial and error.

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TAAS Journal 2019 Journal Article

Human-centric Data Dissemination in the IoP

  • Matteo Mordacchini
  • Marco Conti
  • Andrea Passarella
  • Raffaele Bruno

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed “Internet of People” paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this article, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behaviour in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modeling methodology that combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and countrywide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

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TAAS Journal 2015 Journal Article

Crowdsourcing through Cognitive Opportunistic Networks

  • Matteo Mordacchini
  • Andrea Passarella
  • Marco Conti
  • Stuart M. Allen
  • Martin J. Chorley
  • Gualtiero B. Colombo
  • Vlad Tanasescu
  • Roger M. Whitaker

Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However, the emergence of Opportunistic Networking (ON) has opened up crowdsourcing to the spatial domain. In this article, we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features of the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics—mental models that describe how the human brain handles decision making among complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in their users’ cyberworlds and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.

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TAAS Journal 2013 Journal Article

Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

  • Marco Conti
  • Matteo Mordacchini
  • Andrea Passarella

In the convergence of the Cyber-Physical World, user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, the recognition heuristic, for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.

Details DOI
v2026.09.13