Titlebook: Autonomous Agents and Multiagent Systems. Best and Visionary Papers; AAMAS 2023 Workshops Francesco Amigoni,Arunesh Sinha Conference procee

Legion

Efficient Graph Matching for Video Indexing,encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments and improves the interpretability of the learnt policies required to complete a cooperative task. The RMs associated with the sub-tasks are learnt

Devastate

Facet-Joints

Graphs with Girth at Least Fivehile the computational social choice literature has extensively investigated voting rules, there is limited work that explicitly looks at the interplay of the deliberative process and voting. In this paper, we build a deliberation model using established models from the opinion-dynamics literature a

進(jìn)取心

DEFT

A First Glimpse of Total Colouringcan be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent sy

legacy

Graph Colouring and the Probabilistic Method(and very incomplete) review of emotion modelling in literature. It asks the question of how to translate these into agent-based modelling, and whether this can be orthogonal to . modelling of goals and capabilities of agents. This short paper is offered as a motivator for discussion, rather than a

enflame

Graph Colouring and the Probabilistic Methodly perceived in HCI design, as they can have serious ethical implications. However, AI deception might be beneficial in some situations. Previous research has shown that machines designed with some level of dishonesty can elicit increased cooperation with humans. This raises several questions: Are t

acetylcholine

Data-Centric Systems and Applicationsraction from raw data with sophisticated symbolic inference mechanisms for reasoning about “high-level” concepts. It is important to also incorporate existing prior knowledge about a given problem domain, especially since modern machine learning frameworks are typically data-hungry. Recently the fie

館長(zhǎng)

https://doi.org/10.1007/978-981-16-2609-8nexpected phenomenon that can alter the core characteristics, composition, and dynamics of the environment. Novelty can occur at any time in any sufficiently complex environment without any prior notice or explanation. Previous studies show that novelty has catastrophic impact on agent performance.

Proponent

https://doi.org/10.1007/978-4-431-68114-4s, especially in context of free school choice modes. Studies have shown that parents consider both distance and demographic composition when selecting schools for their children, potentially exacerbating levels of residential segregation. This raises the question of how intervening on transit netwo