Zobrazeno 1 - 10
of 369
pro vyhledávání: '"Luck, Michael"'
Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotteries, and bargaining systems. Fairness in MAS depends on various facto
Externí odkaz:
http://arxiv.org/abs/2410.12889
Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to increase generalisation and human intervention to capture key aspects
Externí odkaz:
http://arxiv.org/abs/2407.04856
Imitation learning is an approach in which an agent learns how to execute a task by trying to mimic how one or more teachers perform it. This learning approach offers a compromise between the time it takes to learn a new task and the effort needed to
Externí odkaz:
http://arxiv.org/abs/2404.19456
Autor:
Battogtokh, Munkhtulga, Xing, Yiwen, Davidescu, Cosmin, Abdul-Rahman, Alfie, Luck, Michael, Borgo, Rita
In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic struct
Externí odkaz:
http://arxiv.org/abs/2403.15492
Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a cumbersome process
Externí odkaz:
http://arxiv.org/abs/2403.00550
The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs). These models have revolutionized NLP tasks, particularly in code generation, aiding developers i
Externí odkaz:
http://arxiv.org/abs/2312.13010
Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In resp
Externí odkaz:
http://arxiv.org/abs/2310.12928
Customising AI technologies to each user's preferences is fundamental to them functioning well. Unfortunately, current methods require too much user involvement and fail to capture their true preferences. In fact, to avoid the nuisance of manually se
Externí odkaz:
http://arxiv.org/abs/2308.02542
Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect them to adhere to their priva
Externí odkaz:
http://arxiv.org/abs/2302.10650