The DOI matches its Crossref record; 52 of the 60 references it cites were found in a registry or an index, and 1 are not checked yet.
A heuristic technique for multi‐agent planning
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Claims other papers make about this one
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In the citing paper
“Traditional AI Agents feature limited learning mechanisms, such as heuristic parameter adjustment [161], [162] or history-informed context retention.”
From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [161]
Citations
Cites
A Theory of Justice: Original Edition
Rawls, J. (1971) · doi:10.4159/9780674042605 · Cited by 9,496
COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS
Vickrey, W. (1961) · The Journal of Finance · doi:10.1111/j.1540-6261.1961.tb02789.x · Cited by 7,722
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
Smith (1980) · IEEE Transactions on Computers · doi:10.1109/tc.1980.1675516 · Cited by 3,716
Counterspeculation, Auctions, and Competitive Sealed Tenders
Vickrey, W. S. (1961) · The Journal of Finance · doi:10.2307/2977633 · Cited by 1,923
Planning for conjunctive goals
Chapman, D. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90092-0 · Cited by 1,112
An analysis of time-dependent planning
Dean, T., & Boddy, M. (1988) · National Conference on Artificial Intelligence · Cited by 789
UCPOP: a sound, complete, partial order planner for ADL
Penberthy, J. S., & Weld, D. S. (1992) · Cited by 676
McAllester, D., & Rosenblitt, D. (1991) · Cited by 525
A Computer Model of Skill Acquisition
Sussman, G. J. (1975) · Cited by 454
Planning as search: A quantitative approach
Korf, R. E. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90051-8 · Cited by 363
Research works printed but not linked: 10
- T. Dean and M. Boddy, An analysis of time-dependent planning, in: Proceedings of the Seventh National Conference on Artificial Intelligence (St. Paul, Minnesota, August 1988) pp. 49–54.
- E.H. Durfee, A cooperative approach to planning for real-time control, in: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (San Diego, California, November 1990) pp. 277–283.
- E. Ephrati, M.E. Pollack and J.S. Rosenschein, Exploitation of decision theory techniques in multi-agent planning (extended abstract), in: Working Notes of the AAAI Spring Symposium on Decision-Theoretic Planning (Stanford, CA, March 1994) pp. 90–98.
- E. Ephrati, M.E. Pollack and S. Ur, Exploitation of decision theory techniques in multi-agent planning (extended abstract), in: Proceedings of The Fourteenth International Joint Conference on Artificial Intelligence (Montreal, Canada, August 1995) pp. 679–685.
- P. Haddawy and S. Hanks, Issues in decision-theoretic planning: Symbolic goals and numeric utilities, in: Proceedings of the 1990 DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control (November 1990) pp. 48–58.
- S. Hanks and R.J. Firby, Issues and architectures for planning and execution, in: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (San Diego, California, November 1990) pp. 59–70.
- S. Kraus, M. Nirkhe and D. Perlis, Deadline-coupled real-time planning, in: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (San Diego, California, November 1990) pp. 100–108.
- A.L. Lansky, Localized search for controlling automated reasoning, in: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (San Diego, California, November 1990) pp. 115–125.
- T. Moehlman and V. Lesser, Cooperative planning and decentralized negotiation in Multi-Fireboss Phoenix, in: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (San Diego, California, November 1990) pp. 144–159.
- R.P. Pope, S.E. Conry and R.A. Mayer, Distributing the planning process in a dynamic environment, in: Proceedings of the Eleventh International Workshop on Distributed Artificial Intelligence (Glen Arbor, Michigan, February 1992) pp. 317–331.
Cited by
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 671
“…example of a tool-augmented AI agent is ChatGPT, which, when unable to answer a query directly, autonomously invokes the Web Search API to retrieve more recent and relevant information, performs reasoning over the retrieved content, and formulates a response based on its understanding [137].”
How Hard Is Bribery in Elections?
Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. A. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2676 · Cited by 236
Llull and Copeland Voting Computationally Resist Bribery and Constructive Control
Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L. A., & Rothe, J. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2697 · Cited by 228
Using complexity to protect elections
Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. A. (2010) · Communications of the ACM · doi:10.1145/1839676.1839696 · Cited by 185
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186
Metadata
- Authors
- Ephrati, E., & Rosenschein, J. S.
- Affiliations
- Bar-Ilan University (IL)
- Hebrew University of Jerusalem (IL)
- Corresponding author
- Ephrati, Eithan
- Rosenschein, Jeffrey S.
- Venue
- Annals of Mathematics and Artificial Intelligence
- Published
- 1997-07
- Volume
- 20
- Issue
- 1-4
- Pages
- 13-67
- ISSN
- 1012-2443 (ISSN-L)
- 1573-7470
- OpenAlex
- W1889721679
- Cited by
- 93
- References
- 60
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 1 · 2021: 4 · 2020: 3 · 2019: 1 · 2018: 2 · 2017: 2 · 2016: 3 · 2015: 5 · 2014: 10 · 2013: 5 · 2012: 7
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Annals of Mathematics and Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Keywords
- multi-agent planning, heuristic planning, subgoal decomposition, distributed artificial intelligence, rational agents
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A heuristic technique for multi‐agent planning. https://app.cochat.ai/papers/10.1023/a:1018924209812
- The paper
- Ephrati, E., & Rosenschein, J. S. (1997). A heuristic technique for multi‐agent planning. Annals of Mathematics and Artificial Intelligence. https://doi.org/10.1023/a%3A1018924209812
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.