Explainable goal-driven agents and robots-a comprehensive review
Recent applications of autonomous agents and robots have brought attention to crucial trust-
related challenges associated with the current generation of artificial intelligence (AI) …
related challenges associated with the current generation of artificial intelligence (AI) …
Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data
In this paper we introduce a new class of software tools engaged in delivering successful
explanations of complex processes on top of basic Explainable AI (XAI) software systems …
explanations of complex processes on top of basic Explainable AI (XAI) software systems …
Attention Loss Adjusted Prioritized Experience Replay
Z Chen, H Li, R Wang - arXiv preprint arXiv:2309.06684, 2023 - arxiv.org
Prioritized Experience Replay (PER) is a technical means of deep reinforcement learning by
selecting experience samples with more knowledge quantity to improve the training rate of …
selecting experience samples with more knowledge quantity to improve the training rate of …
Generating user-centred explanations via illocutionary question answering: From philosophy to interfaces
We propose a new method for generating explanations with Artificial Intelligence (AI) and a
tool to test its expressive power within a user interface. In order to bridge the gap between …
tool to test its expressive power within a user interface. In order to bridge the gap between …
Autonomous Driving via Knowledge-Enhanced Safe Reinforcement Learning
Recently, the autonomous driving technology is at a critical phase evolving from typical,
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …
How to improve the explanatory power of an intelligent textbook: a case study in legal writing
Explanatory processes are at the core of scientific investigation, legal reasoning, and
education. However, effectively explaining complex or large amounts of information, such as …
education. However, effectively explaining complex or large amounts of information, such as …
Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations
RS Sullivan, L Longo - Machine Learning and Knowledge Extraction, 2023 - mdpi.com
Reinforcement Learning (RL) has shown promise in optimizing complex control and
decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability …
decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability …
Crossover in mutation oriented norm evolution
B Lv, X Wang, R Zhang - Complex & Intelligent Systems, 2024 - Springer
Norms are a coordination mechanism. They control agents' behavior in a multiagent system
(MAS) and need to evolve to cope with changing environments. Mutation oriented norm …
(MAS) and need to evolve to cope with changing environments. Mutation oriented norm …
How to explain: from theory to practice
F Sovrano - 2023 - amsdottorato.unibo.it
Today we live in an age where the internet and artificial intelligence allow us to search for
information through impressive amounts of data, opening up revolutionary new ways to …
information through impressive amounts of data, opening up revolutionary new ways to …