標題: Titlebook: Explainable and Transparent AI and Multi-Agent Systems; 5th International Wo Davide Calvaresi,Amro Najjar,Kary Fr?mling Conference proceedi [打印本頁] 作者: DUBIT 時間: 2025-3-21 20:07
書目名稱Explainable and Transparent AI and Multi-Agent Systems影響因子(影響力)
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書目名稱Explainable and Transparent AI and Multi-Agent Systems網絡公開度學科排名
書目名稱Explainable and Transparent AI and Multi-Agent Systems被引頻次
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書目名稱Explainable and Transparent AI and Multi-Agent Systems讀者反饋
書目名稱Explainable and Transparent AI and Multi-Agent Systems讀者反饋學科排名
作者: doxazosin 時間: 2025-3-21 22:07
Handbook of Youth Suicide Preventionstem predictability. Previous work has proposed an explanation mechanism for Belief-Desire-Intention (BDI) agents that uses folk psychological concepts, specifically beliefs, desires, and valuings. In this paper we evaluate this mechanism by conducting a survey. We consider a number of explanations,作者: Interferons 時間: 2025-3-22 01:56
The Victimization of Children and Youth 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作者: 貧窮地活 時間: 2025-3-22 06:18
Takashi Ogawa,Akihiko Masuda,Kayla Sargentn harm and, in the worst cases, death. Explainable Artificial Intelligence (XAI) tries to produce human-understandable explanations for AI decisions. However, most XAI systems prioritize factors such as technical complexities and research-oriented goals over end-user needs, risking information overl作者: Pander 時間: 2025-3-22 10:33
S. De Spirt,W. Stahl,U. Heinrichg 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作者: 供過于求 時間: 2025-3-22 16:14
Handbook of nutrition, diet and sleepenarios. This is the main reason behind many approaches focusing on local post-hoc explanations recently proposed by the XAI community. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, mainly for the lack of a general metric作者: 供過于求 時間: 2025-3-22 20:15 作者: 譏笑 時間: 2025-3-23 00:46
Handbook of the Brief Psychotherapiesety 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作者: Homocystinuria 時間: 2025-3-23 03:06 作者: 搖曳的微光 時間: 2025-3-23 07:01 作者: intention 時間: 2025-3-23 10:08
Handbook of the History of General Topologyr the tumor is caused by HPV infection (HPV status) is essential to select the appropriate treatment. Therefore, developing non-invasive, transparent (trustworthy), and reliable methods is imperative to tailor the treatment to patients based on their status. Some studies have tried to use radiomics 作者: Expand 時間: 2025-3-23 16:54 作者: Fraudulent 時間: 2025-3-23 19:43 作者: obstruct 時間: 2025-3-24 00:52
Studies in the History of Law and Justiceivities and decisions. Thus, focusing on design and interaction that, in addition to being functional, foster users’ acceptance and trust is increasingly necessary. Human-computer interaction (HCI) and human-robot interaction (HRI) studies focused more and more on the exploitation of communication m作者: 冰雹 時間: 2025-3-24 04:47 作者: 糾纏 時間: 2025-3-24 07:51
Conference proceedings 202315 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions.?The workshop focuses on?Explainable Agents and multi-agent systems;?Explainable Machine Learning; and Cross-domain applied XAI...?..作者: Headstrong 時間: 2025-3-24 14:18
0302-9743 23.??.The 15 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions.?The workshop focuses on?Explainable Agents and multi-agent systems;?Explainable Machine Learning; and Cross-domain applied XAI...?..978-3-031-40877-9978-3-031-40878-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 江湖郎中 時間: 2025-3-24 18:11
https://doi.org/10.1007/978-3-031-40878-6Multiagent Systems; Machine Learning; Compilers and Interpreters; Natural Language Processing (NLP); The作者: Endometrium 時間: 2025-3-24 21:12
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/319307.jpg作者: 調色板 時間: 2025-3-24 23:55 作者: accessory 時間: 2025-3-25 07:06 作者: 陰險 時間: 2025-3-25 08:57
The Hemopoietic Extracellular Matrix,Contextual Importance and Utility (CIU) is a model-agnostic method for post-hoc explanation of prediction outcomes. In this paper we describe and show new functionality in the R implementation of CIU for tabular data. Much of that functionality is specific to CIU and goes beyond the current state of the art.作者: objection 時間: 2025-3-25 13:58 作者: Palpate 時間: 2025-3-25 19:37
0302-9743 23.??.The 15 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions.?The workshop focuses on?Explainable Agents and multi-agent systems;?Explainable Machine Learning; and Cross-domain applied XAI...?..978-3-031-40877-9978-3-031-40878-6Series ISSN 0作者: Expediency 時間: 2025-3-25 23:19
Upasana Bagchi,K. Jayasankara Reddy system execution, which describes action execution events and external events (represented as beliefs). We present an empirical evaluation, which suggests that our approach to mining and validating belief-based explanations can be practical.作者: MURKY 時間: 2025-3-26 03:02
Handbook of Youth Suicide Preventionticularly likely to be preferred by the study participants, whereas those explanations that include links are least likely to be preferred. We also found evidence that single-factor explanations, as used in some previous work, are too short.作者: 上釉彩 時間: 2025-3-26 08:21
Takashi Ogawa,Akihiko Masuda,Kayla Sargent rule-based AI systems is presented, allowing the system to explain its decisions by engaging in “Why?” and “Why not?” questions and answers. We establish formal properties of this framework and present a small user study with encouraging results that compares dialogue-based explanations with proof trees produced by the AI System.作者: 起波瀾 時間: 2025-3-26 09:39 作者: deactivate 時間: 2025-3-26 16:10
Handbook of the History of General Topologyations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.作者: 出處 時間: 2025-3-26 17:01
Handbook of the History of General Topologyxploiting new classes of observations. We use the notation of capabilities in the well-known Belief-Desire-Intention (BDI) agents programming as an ideal platform to discuss our work. To validate our approach, we show the implementation of our approach using practical examples from the Monroe Plan Corpus.作者: Hiatus 時間: 2025-3-27 00:55 作者: 柳樹；枯黃 時間: 2025-3-27 03:47
Mining and?Validating Belief-Based Agent Explanations system execution, which describes action execution events and external events (represented as beliefs). We present an empirical evaluation, which suggests that our approach to mining and validating belief-based explanations can be practical.作者: Latency 時間: 2025-3-27 07:14
Evaluating a?Mechanism for?Explaining BDI Agent Behaviourticularly likely to be preferred by the study participants, whereas those explanations that include links are least likely to be preferred. We also found evidence that single-factor explanations, as used in some previous work, are too short.作者: BOLT 時間: 2025-3-27 12:09
Dialogue Explanations for Rule-Based AI Systems rule-based AI systems is presented, allowing the system to explain its decisions by engaging in “Why?” and “Why not?” questions and answers. We establish formal properties of this framework and present a small user study with encouraging results that compares dialogue-based explanations with proof trees produced by the AI System.作者: stressors 時間: 2025-3-27 16:04 作者: 反抗者 時間: 2025-3-27 19:43 作者: hematuria 時間: 2025-3-28 00:15 作者: insipid 時間: 2025-3-28 03:26 作者: synovium 時間: 2025-3-28 07:52 作者: 歌唱隊 時間: 2025-3-28 10:33 作者: Autobiography 時間: 2025-3-28 15:42 作者: CREEK 時間: 2025-3-28 20:47
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作者: Canopy 時間: 2025-3-29 01:24 作者: 聽覺 時間: 2025-3-29 05:55
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作者: 半圓鑿 時間: 2025-3-29 07:57 作者: LUCY 時間: 2025-3-29 11:48
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作者: Nebulous 時間: 2025-3-29 16:13
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作者: Console 時間: 2025-3-29 21:15
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作者: 填料 時間: 2025-3-30 01:44 作者: 裂隙 時間: 2025-3-30 05:33 作者: Expurgate 時間: 2025-3-30 09:04 作者: myalgia 時間: 2025-3-30 13:30
Leveraging Imperfect Explanations for?Plan Recognition Problemsepair, etc. Hence, real-life Plan Recognition (PR) approaches are required to deal with different classes of observations (e.g., exogenous actions, switching between activities, and missing observations). Many approaches to PR consider these classes of observations, but none have dealt with them as 作者: DEAWL 時間: 2025-3-30 17:49 作者: 煩憂 時間: 2025-3-30 23:15
Enhancing Wearable Technologies for?Dementia Care: A Cognitive Architecture Approachs have increasingly been used to monitor, diagnose, and assist these patients. The present paper argues that the benefits current and future wearable devices provide to dementia patients could be enhanced with cognitive architectures. It proposes such an architecture, establishing connections betwee作者: induct 時間: 2025-3-31 03:51
Explainable and Transparent AI and Multi-Agent Systems5th International Wo作者: muffler 時間: 2025-3-31 08:40
The Victimization of Children and Youthre-usable software library, namely ., which is meant to support developers willing to build explainable MAS leveraging our protocol. Finally, we discuss how custom notions of recommendation and explanation can be easily plugged into ..作者: 性別 時間: 2025-3-31 10:09
