Titlebook: Explainable and Transparent AI and Multi-Agent Systems; 5th International Wo Davide Calvaresi,Amro Najjar,Kary Fr?mling Conference proceedi

Autobiography

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A General-Purpose Protocol for?Multi-agent Based Explanations agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation. However, it does not pres

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Estimating Causal Responsibility for?Explaining Autonomous Behaviorg offer several theoretical benefits when exact inference can be applied. Furthermore, users overwhelmingly prefer the resulting causal explanations over other state-of-the-art systems. In this work, we focus on one such method, ., and its approximate versions that drastically reduce compute load an

半圓鑿

LUCY

Bottom-Up and?Top-Down Workflows for?Hypercube- And Clustering-Based Knowledge Extractorsressive predictive performances. However, they act as black boxes (BBs) from the human standpoint, so they cannot be entirely trusted in critical applications unless there exists a method to extract symbolic and human-readable knowledge out of them..In this paper we analyse a recurrent design adopte

Nebulous

Imperative Action Masking for?Safe Exploration in?Reinforcement Learningety hazards, not necessarily in the next state but in the future. Therefore, it is essential to evaluate each action beforehand to ensure safety. The exploratory actions and the actions proposed by the RL agent could also be unsafe during training and in the deployment phase. In this work, we have p

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Reinforcement Learning in?Cyclic Environmental Changes for?Agents in?Non-Communicative Environments:-communicative and dynamic environment. Profit minimizing reinforcement learning with the oblivion of memory (PMRL-OM) enables agents to learn a co-operative policy using learning dynamics instead of communication information. It enables the agents to adapt to the dynamics of the other agents’ behav

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