Explainable Agency for Intelligent Autonomous Systems
Open accessAbstract
As intelligent agents become more autonomous, sophisti- cated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player’s path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more selfreliant, people will require that they explain their behaviors on demand. The more impressive these agents’ abilities, the more essential that we be able to understand them.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some contributions include the ability of a model to be interactive with the user as one of the goals targeted by an explainable ML model.
“some contributions [50, 59] include the ability of a model to be interactive with the user as one of the goals targeted by an explainable ML model.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [59]
Cites
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,883 · see citing works
Building Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523 · see citing works
Explanation in Second Generation Expert Systems
Swartout, W. R., & Moore, J. D. (1993) · Second Generation Expert Systems · doi:10.1007/978-3-642-77927-5_24 · Cited by 160 · see citing works
Agents that Learn to Explain Themselves
Johnson, W. L. (1994) · Cited by 93 · see citing works
Plan Explainability and Predictability for Cobots.Preprint
Zhang, Y., Zhuo, H. H., & Subbarao Kambhampati, B. (2015) · arXiv (Cornell University) · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783 · see citing works
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works
Explainable Agents and Robots: Results from a Systematic Literature Review
Anjomshoae, S., Najjar, A., Calvaresi, D., & Främling, K. (2019) · Adaptive Agents and Multi-Agent Systems · doi:10.65109/kczb5817 · Cited by 402 · see citing works
Explainability in human–agent systems
Rosenfeld, A., & Richardson, A. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09408-y · Cited by 228 · see citing works
Building Ethics into Artificial Intelligence
Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/779 · Cited by 210 · see citing works
Explainable PlanningPreprint
Fox, M., Long, D. P., & Magazzeni, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.10256 · Cited by 148 · see citing works
Hassan, M. R., Islam, M. F., Uddin, M. Z., Ghoshal, G., Hassan, M. M., Huda, S., & Fortino, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.030 · Cited by 147 · see citing works
Trustworthy AI and robotics: Implications for the AEC industry
Emaminejad, N., & Akhavian, R. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104298 · Cited by 152 · see citing works
Levels of explainable artificial intelligence for human-aligned conversational explanations
Dazeley, R., Vamplew, P. W., Foale, C., Young, C., Aryal, S., & Cruz, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103525 · Cited by 122 · see citing works
IEEE P7001: A Proposed Standard on Transparency
Winfield, A. F. T., Booth, S., Dennis, L. A., Egawa, T., Hastie, H., Jacobs, N. J., Muttram, R. I., Olszewska, J. I., Rajabiyazdi, F., Theodorou, A. A., et al. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.665729 · Cited by 110 · see citing works
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Nguyen, T. N., Ponciroli, R., Bruck, P. M., Esselman, T. C., Rigatti, J. A., & Vilim, R. (2022) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2022.109002 · Cited by 85 · see citing works
Floridi, L. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00858-9 · Cited by 81 · see citing works
Plan Explanations as Model Reconciliation -- An Empirical Study
Chakraborti, T., Sreedharan, S., Grover, S., & Subbarao Kambhampati, B. (2019) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673193 · Cited by 79 · see citing works
Viganò, L., & Magazzeni, D. (2020) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw51379.2020.00045 · Cited by 78 · see citing works
The Emerging Landscape of Explainable Automated Planning & Decision Making
Chakraborti, T., Sreedharan, S., & Subbarao Kambhampati, B. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/669 · Cited by 107 · see citing works
Designing Co-Creative AI for Public Spaces
Long, D., Jacob, M., & Magerko, B. (2019) · Proceedings of the 2019 on Creativity and Cognition · doi:10.1145/3325480.3325504 · Cited by 70 · see citing works
Explainable reinforcement learning for broad-XAI: a conceptual framework and survey
Dazeley, R., Vamplew, P. W., & Cruz, F. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08423-1 · Cited by 68 · see citing works
Saffarizadeh, K., Keil, M., & Maruping, L. M. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2376382 · Cited by 61 · see citing works
Ethically Responsible Machine Learning in Fintech
Rizinski, M., Peshov, H., Mishev, K., Chitkushev, L. T., Vodenska, I., & Trajanov, D. (2022) · IEEE Access · doi:10.1109/access.2022.3202889 · Cited by 69 · see citing works
Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X
Tenhundfeld, N. L., de Visser, E. J., Haring, K. S., Ries, A. J., Finomore, V. S., & Tossell, C. C. (2019) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343419869083 · Cited by 52 · see citing works
Human-Swarm-Teaming Transparency and Trust Architecture
Hepworth, A., Baxter, D., Hussein, A., Yaxley, K. J., Debie, E. S., & Abbass, H. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003545 · Cited by 40 · see citing works
Foundations of explanations as model reconciliation
Sreedharan, S., Chakraborti, T., & Subbarao Kambhampati, B. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103558 · Cited by 51 · see citing works
Hierarchical Expertise Level Modeling for User Specific Contrastive Explanations
Sreedharan, S., Srivastava, S., & Kambhampati, S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/671 · Cited by 50 · see citing works
Ghosh, S., Burachas, G., Ray, A., & Ziskind, A. (2019) · arXiv.org · doi:10.48550/arxiv.1902.05715 · Cited by 69 · see citing works
Pereira, G. V., Estévez, E., Cardona, D., Chesñevar, C. I., Collazzo, P., Cunha, M. A., Diniz, E. H., Ferraresi, A. A., Fischer, F. M., Garcia, F. C. O., et al. (2020) · Sustainability · doi:10.3390/su12020718 · Cited by 51 · see citing works
Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks
Calvaresi, D., Calbimonte, J.-P., Dubosson, F., Najjar, A., & Schumacher, M. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3352532 · Cited by 43 · see citing works
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Gao, X.-F., Gong, R., Zhao, Y.-Z., Wang, S., Shu, T., & Zhu, S.-C. (2020) · IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/ro-man47096.2020.9223595 · Cited by 48 · see citing works
Amir, O., Doshi‐Velez, F., & Sarne, D. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09418-w · Cited by 39 · see citing works
Towards a Theory of Explanations for Human–Robot Collaboration
Sridharan, M., & Meadows, B. (2019) · KI - Künstliche Intelligenz · doi:10.1007/s13218-019-00616-y · Cited by 63 · see citing works
Self-citationUnderstandable and trustworthy explainable robots: A sensemaking perspective
Papagni, G., & Koeszegi, S. T. (2020) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2021-0002 · Cited by 39 · see citing works
Conversational Interfaces for Explainable AI: A Human-Centred Approach
Jentzsch, S. F., Höhn, S., & Hochgeschwender, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30391-4_5 · Cited by 37 · see citing works
Masialeti, M., Talaei‐Khoei, A., & Yang, A. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102779 · Cited by 36 · see citing works
Memory-Based Explainable Reinforcement Learning
Cruz, F., Dazeley, R., & Vamplew, P. (2019) · Australasian Conference on Artificial Intelligence · doi:10.1007/978-3-030-35288-2_6 · Cited by 36 · see citing works
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario
Cruz, F., Dazeley, R., Vamplew, P., & Moreira, I. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06425-5 · Cited by 34 · see citing works
Deep learning, transparency, and trust in human robot teamwork
Lewis, M., Li, H., & Sycara, K. P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819472-0.00014-9 · Cited by 29 · see citing works
The Emerging Landscape of Explainable AI Planning and Decision MakingPreprint
Chakraborti, T., Sreedharan, S., & Subbarao Kambhampati, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11697 · Cited by 29 · see citing works
Secure embedded intelligence in nuclear systems: Framework and methods
Garcia, H. E., Aumeier, S. E., Rashdan, A. Y. A., & Rolston, B. (2020) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2019.107261 · Cited by 28 · see citing works
Towards Explainable AI Planning as a ServicePreprint
Cashmore, M., Collins, A., Krarup, B., Krivić, S., Magazzeni, D., & Smith, D. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05059 · Cited by 28 · see citing works
Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks
Naiseh, M., Jiang, N., Ma, J., & Ali, R. (2020) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-030-50316-1_13 · Cited by 27 · see citing works
Explainable, Normative, and Justified Agency
Langley, P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019775 · Cited by 25 · see citing works
Verhagen, R., Neerincx, M. A., & Tielman, M. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82017-6_8 · Cited by 26 · see citing works
Evaluating the impact of AI on insurance: the four emerging AI- and data-driven business models
Zarifis, A., Holland, C. P., & Milne, A. K. L. (2019) · Emerald Open Research · doi:10.1108/eor-01-2023-0001 · Cited by 25 · see citing works
A framework for step-wise explaining how to solve constraint satisfaction problems
Bogaerts, B., Gamba, E., & Guns, T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103550 · Cited by 23 · see citing works
Balancing Explicability and Explanation in Human-Aware PlanningPreprint
Chakraborti, T., Sreedharan, S., & Subbarao Kambhampati, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00543 · Cited by 24 · see citing works
Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract)
Hoffmann, J., & Magazzeni, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31423-1_9 · Cited by 22 · see citing works
Kaadoud, I. C., Bennetot, A., Mawhin, B., Charisi, V., & Díaz-Rodríguez, N. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.08.002 · Cited by 21 · see citing works
Wang, J., Malawade, A. V., Zhou, J., Yu, S.-Y., & Faruque, M. A. A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00732 · Cited by 21 · see citing works
Computer aided functional style identification and correction in modern russian texts
Savchenko, E. V., & Lazebnik, T. (2022) · Journal of Data Information and Management · doi:10.1007/s42488-021-00062-2 · Cited by 20 · see citing works
Visualizations for an Explainable Planning Agent
Chakraborti, T., Fadnis, K. P., Talamadupula, K., Dholakia, M., Srivastava, B., Kephart, J., & Bellamy, R. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/849 · Cited by 34 · see citing works
White Paper Machine Learning in Certified SystemsPreprint
Delseny, H., Gabreau, C., Gauffriau, A., Beaudouin, B., Ponsolle, L., Alecu, L., Bonnin, H., Beltran, B., Duchel, D., Ginestet, J.-B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10529 · Cited by 20 · see citing works
ICM: An Intuitive Model Independent and Accurate Certainty Measure for Machine Learning
van der Waa, J., van Diggelen, J., Neerincx, M. A., & Raaijmakers, S. (2018) · International Conference on Agents and Artificial Intelligence · doi:10.5220/0006542603140321 · Cited by 17 · see citing works
Intelligent robot-assisted evacuation: A review
Bahamid, A., Ibrahim, A. M., Ibrahim, A. N., Zahurin, I. Z., & Wahid, A. N. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1706/1/012159 · Cited by 18 · see citing works
Castellini, A., Marchesini, E., & Farinelli, A. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104382 · Cited by 18 · see citing works
Using state abstractions to compute personalized contrastive explanations for AI agent behavior
Sreedharan, S., Srivastava, S., & Subbarao Kambhampati, B. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103570 · Cited by 18 · see citing works
Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models
Zarifis, A., Holland, C. P., & Milne, A. K. L. (2019) · Emerald Open Research · doi:10.35241/emeraldopenres.13249.1 · Cited by 17 · see citing works
Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions
Khoozani, Z. S., Sabri, A. Q. M., Seng, W. C., Seera, M., & Eg, K. Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17666-y · Cited by 18 · see citing works
Towards a Framework Combining Machine Ethics and Machine Explainability
Baum, K. L., Hermanns, H., & Speith, T. (2019) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.286.4 · Cited by 16 · see citing works
Balancing Explicability and Explanations in Human-Aware Planning
Chakraborti, T., Sreedharan, S., & Subbarao Kambhampati, B. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/185 · Cited by 15 · see citing works
Zou, Y., Zhang, Z., Michael Backes, A., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04872 · Cited by 15 · see citing works
Online Explanation Generation for Planning Tasks in Human-Robot Teaming
Zakershahrak, M., Gong, Z., Sadassivam, N., & Zhang, Y. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341792 · Cited by 22 · see citing works
Abdulrahman, A., Richards, D., & BILGIN, A. A. B. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09553-x · Cited by 14 · see citing works
From Machine Ethics To Machine Explainability and Back
Baum, K. L., Hermanns, H., & Speith, T. (2018) · Publications of the UdS (Saarland University) · doi:10.22028/d291-48225 · Cited by 14 · see citing works
Distal Explanations for Model-free Explainable Reinforcement LearningPreprint
Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10284 · Cited by 14 · see citing works
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
Gamba, E., Bogaerts, B., & Guns, T. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14260 · Cited by 13 · see citing works
Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP
Onyekpe, U., Lu, Y., Apostolopoulou, E., Palade, V., Eyo, E., & Kanarachos, S. A. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_8 · Cited by 13 · see citing works
SAIL: A Social Artificial Intelligence Layer for Human-Machine Teaming
van der Vecht, B., van Diggelen, J., Peeters, M. M. M., Barnhoorn, J., & van der Waa, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94580-4_21 · Cited by 13 · see citing works
BEN: An Agent Architecture for Explainable and Expressive Behavior in Social Simulation
Bourgais, M., Taillandier, P., & Vercouter, L. (2019) · EXTRAAMAS@AAMAS · doi:10.1007/978-3-030-30391-4_9 · Cited by 12 · see citing works
Evaluating Explanations by Cognitive Value
Chander, A., & Srinivasan, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_23 · Cited by 13 · see citing works
Explainable artificial intelligence for enhanced living environments: A study on user perspective
Wickramanayake, S., Rasnayaka, S., Gamage, M., Meedeniya, D., & Perera, I. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.10.002 · Cited by 12 · see citing works
A Multi-level Explainability Framework for Engineering and Understanding BDI Agents
Yan, E., Burattini, S., Hübner, J. F., & Ricci, A. (2025) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-025-09689-6 · Cited by 12 · see citing works
Step-Wise Explanations of Constraint Satisfaction Problems
Bogaerts, B., Gamba, E., Claes, J., & Guns, T. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200149 · Cited by 12 · see citing works
Reason Explanation for Encouraging Behaviour Change Intention
Abdulrahman, A., Richards, D., & BILGIN, A. A. B. (2021) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/ugaq3270 · Cited by 12 · see citing works
Risk-aware shielding of Partially Observable Monte Carlo Planning policies
Mazzi, G., Castellini, A., & Farinelli, A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103987 · Cited by 12 · see citing works
Explaining Sympathetic Actions of Rational Agents
Kampik, T., Nieves, J. C., & Lindgren, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30391-4_4 · Cited by 12 · see citing works
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario
(14618705), F. C., Dazeley, R., (13351392), P. V., & (13430100), I. M. (2024) · Figshare · Cited by 12 · see citing works
Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming
Zakershahrak, M., Marpally, S. R., Sharma, A., Gong, Z., & Zhang, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561762 · Cited by 12 · see citing works
Abdulrahman, A., & Richards, D. (2021) · Multimodal Technologies and Interaction · doi:10.3390/mti5090056 · Cited by 11 · see citing works
Brachman, M., Pan, Q., Do, H. J., Dugan, C., Chaudhary, A., Johnson, J. M., Rai, P., Chakraborti, T., Gschwind, T., Laredo, J. A., et al. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584088 · Cited by 11 · see citing works
Culture-Based Explainable Human-Agent Deconfliction
Raymond, A., Güneş, H., & Prorok, A. (2020) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/dwbu7211 · Cited by 11 · see citing works
Abdulrahman, A., Richards, D., & BILGIN, A. A. B. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2073269 · Cited by 11 · see citing works
Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics
Mota, T., Sridharan, M., & Leonardis, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66412-1_14 · Cited by 11 · see citing works
Self-citationExplainable Goal-Driven Agents and Robots -- A Comprehensive Review and New FrameworkPreprint
Fatai, S., Loo, C. K., Kerzel, M., & Wermter, S. (2020) · arXiv (Cornell University) · Cited by 11 · see citing works
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI Interaction
Sreedharan, S., Kulkarni, A., Smith, D. E., & Subbarao Kambhampati, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/625 · Cited by 10 · see citing works
Explaining Multimodal Errors in Autonomous Vehicles
Gilpin, L. H., Penubarthi, V., & Kagal, L. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564178 · Cited by 10 · see citing works
Designing Interactions with Intention-Aware Gaze-Enabled Artificial Agents
Newn, J., Singh, R., Allison, F., Madumal, P., Velloso, E., & Vetere, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29384-0_17 · Cited by 9 · see citing works
A Framework for Engineering Human/Agent Teaming Systems
Evertsz, R., & Thangarajah, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5629 · Cited by 9 · see citing works
Argumentation-Based Agents that Explain Their Decisions
Morveli-Espinoza, M., Possebom, A. T., & Tacla, C. A. (2019) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2019.00088 · Cited by 9 · see citing works
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
Gamba, E., Bogaerts, B., & Guns, T. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/191 · Cited by 8 · see citing works
Sreedharan, S., Chakraborti, T., Muise, C. J., & Subbarao Kambhampati, B. (2019) · arXiv (Cornell University) · Cited by 8 · see citing works
Online Explanation Generation for Human-Robot TeamingPreprint
Zakershahrak, M., Gong, Z., Sadassivam, N., & Zhang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.06418 · Cited by 8 · see citing works
Machine Reasoning ExplainabilityPreprint
Čyras, K., Badrinath, R., Mohalik, S. K., Mujumdar, A. P., Nikou, A., Previti, A., Sundararajan, V., & Feljan, A. V. (2020) · arXiv (Cornell University) · doi:10.4230/lipics.cp.2026.62 · Cited by 11 · see citing works
Hazardous machinery: The assignment of agency and blame to robots versus non-autonomous machines
Dawtry, R. J., & Callan, M. J. (2023) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2023.104582 · Cited by 7 · see citing works
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Mazzi, G., Castellini, A., & Farinelli, A. (2021) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v31i1.15968 · Cited by 7 · see citing works
Robot explanatory narratives of collaborative and adaptive experiences
Olivares‐Alarcos, A., Andriella, A., Foix, S., & Alenyà, G. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161359 · Cited by 7 · see citing works
A Bayesian Account of Measures of Interpretability in Human-AI InteractionPreprint
Sreedharan, S., Kulkarni, A., Chakraborti, T., Smith, D. E., & Subbarao Kambhampati, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10920 · Cited by 7 · see citing works
Searching for Explainable Solutions in Sudoku
Björnsson, Y., Helgason, S., & Pálsson, A. (2021) · IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9618900 · Cited by 6 · see citing works
- Authors
- Langley, P., Meadows, B., Sridharan, M., & Choi, D.
- Affiliations
- University of Auckland (NZ)
- University of Kansas (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-11
- Volume
- 31
- Issue
- 2
- Pages
- 4762-4763
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2604602969
- Cited by
- 156
- References
- 5
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 10 · 2023: 14 · 2022: 15 · 2021: 25 · 2020: 28 · 2019: 31 · 2018: 11 · 2017: 4
- FWCI
- 9.23
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- explainable AI, autonomous agents, decision explanation, human-robot interaction, trust in autonomous systems, autonomous vehicles, model interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
