找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
樓主: DUBIT
41#
發(fā)表于 2025-3-28 15:42:06 | 只看該作者
42#
發(fā)表于 2025-3-28 20:47:22 | 只看該作者
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
43#
發(fā)表于 2025-3-29 01:24:24 | 只看該作者
44#
發(fā)表于 2025-3-29 05:55:11 | 只看該作者
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
45#
發(fā)表于 2025-3-29 07:57:24 | 只看該作者
46#
發(fā)表于 2025-3-29 11:48:15 | 只看該作者
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
47#
發(fā)表于 2025-3-29 16:13:43 | 只看該作者
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
48#
發(fā)表于 2025-3-29 21:15:59 | 只看該作者
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
49#
發(fā)表于 2025-3-30 01:44:12 | 只看該作者
50#
發(fā)表于 2025-3-30 05:33:01 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-12 13:30
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
合水县| 汝州市| 汤阴县| 灵宝市| 宁远县| 文成县| 买车| 天峻县| 桃江县| 南投市| 舒城县| 武穴市| 壶关县| 霍邱县| 黑龙江省| 盐亭县| 盐边县| 盖州市| 漳平市| 玉溪市| 布拖县| 河东区| 拜泉县| 新河县| 中宁县| 进贤县| 织金县| 衡阳市| 中方县| 甘孜县| 盐池县| 宜宾县| 河池市| 庆安县| 虞城县| 页游| 永济市| 孟村| 马山县| 乌拉特中旗| 洛浦县|