L
Liz Sonenberg
Researcher at University of Melbourne
Publications - 147
Citations - 3692
Liz Sonenberg is an academic researcher from University of Melbourne. The author has contributed to research in topics: Negotiation & Autonomous agent. The author has an hindex of 28, co-authored 143 publications receiving 3330 citations. Previous affiliations of Liz Sonenberg include British University in Dubai & Australian Artificial Intelligence Institute.
Papers
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Journal ArticleDOI
Argumentation-based negotiation
Iyad Rahwan,Sarvapali D. Ramchurn,Nicholas R. Jennings,Peter McBurney,Simon Parsons,Liz Sonenberg +5 more
TL;DR: This article provides a conceptual framework through which the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents are outlined, and surveys and evaluates existing proposed techniques in the literature.
Posted Content
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences.
TL;DR: From a light scan of literature, it is demonstrated that there is considerable scope to infuse more results from the social and behavioural sciences into explainable AI, and some key results from these fields that are relevant to explainableAI are presented.
Book ChapterDOI
Planned Team Activity
TL;DR: A language for representing joint plans for teams of agents, how agents can organize the formation of a suitably skilled team to achieve a joint goal is described, and how such a team can execute these plans to generate complex, synchronized team activity is explained.
Journal Article
Towards Socially Sophisticated BDI Agents
Journal ArticleDOI
Explainable Reinforcement Learning through a Causal Lens
TL;DR: This paper presents an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest and shows that causal model explanations perform better on these measures compared to two other baseline explanation models.