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Alistair Knott

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.

3 papers
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3

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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AIJ Journal 2008 Journal Article

Multi-agent human–machine dialogue: issues in dialogue management and referring expression semantics

  • Alistair Knott
  • Peter Vlugter

This paper presents an implemented model of dialogue management for simple dialogues involving multiple speakers. In our model, the user is one speaker, and the system ‘plays’ a number of other speakers. We present a number of principles governing dialogue management in such cases, which relate to turn-taking and the identification of the addressees of utterances. We also consider how to extend a syntactic and semantic treatment of first- and second-person personal pronouns, and of addressee terms, in order to deal with the multi-speaker scenario. We give some examples of our current system, and conclude by outlining some extensions of the system to include disagreements, interruptions, and private communication between subgroups of dialogue participants.

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