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Principles of Artificial Intelligence
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“The most widely accepted architecture for robotic systems is known as “sense, plan, act” (Nilsson, 1982).”
From Lessons for Robotics From the Control Architecture of the Octopus (2022) · cites this paper as Nilsson, 1982
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Items 101–200 of 1,002
Brill, T. M., Munoz, L., & Miller, R. J. (2019) · Journal of Marketing Management · doi:10.1080/0267257x.2019.1687571 · Cited by 291
Selection of views to materialize in a data warehouse
Gupta, H., & Mumick, I. S. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.16 · Cited by 282
“If an A heuristic expands nodes in the increasing order of their gðxÞ þ hðxÞ values, it is known ([16]) that”
BackgroundScheduling in sports: An annotated bibliography
Kendall, G., Knust, S., Ribeiro, C. C., et al. (2009) · Computers & Operations Research · doi:10.1016/j.cor.2009.05.013 · Cited by 282
Automated deduction by theory resolution
Stickel, M. (1985) · Journal of automated reasoning · doi:10.1007/bf00244275 · Cited by 114
Artificial Intelligence in the 21st Century
Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, I. (2018) · IEEE Access · doi:10.1109/access.2018.2819688 · Cited by 270
“Due to the historical development, AI has been utilized into several major subjects including computer vision, natural language processing, the science of cognition and reasoning, robotics, game theory, and machine learning since the 1980s [6], [7].”
BackgroundOptimal and Efficient Path Planning for Unknown and Dynamic Environments
Stentz, A. (1993) · Cited by 272
The Single Machine Early/Tardy Problem
Ow, P. S., & Morton, T. E. (1989) · Management Science · doi:10.1287/mnsc.35.2.177 · Cited by 266
“They have mainly been developed and studied by researchers in Operations Research and Artificial Intelligence e.g. Barr and Feigenbaum (1981), Lawler and Woods (1966) and Nilsson (1980).”
BackgroundBennewitz, M., Burgard, W., & Thrun, S. (2002) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00256-7 · Cited by 253
“Our system applies the A∗ procedure [17] to compute the cost-optimal paths for the individual robots.”
BackgroundMethodField D*: An Interpolation-Based Path Planner and Replanner
Ferguson, D., & Stentz, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-48113-3_22 · Cited by 255
“As mentioned previously, a number of efficient algorithms exist for performing this planning, such as A* for initial planning and D* and its variants for replanning [5, 18, 26, 8].”
MethodCapacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results
Lu, Q., George, B., & Shekhar, S. (2005) · Lecture notes in computer science · doi:10.1007/11535331_17 · Cited by 252
“We also explored the possibility of formulation of a new optimal algorithm using A* search[28,29].”
MethodPlanning using a temporal world model
Allen, J. F., & Koomen, J. A. G. M. (1983) · Cited by 263
Inferring declarative requirements specifications from operational scenarios
van Lamsweerde, A., & Willemet, L. (1998) · IEEE Transactions on Software Engineering · doi:10.1109/32.738341 · Cited by 250
“A second one, not explored in this paper, is to aggregate recurring subsequences of interaction events into interaction chunks, corresponding to macro-operators in the planning sense [ 55 ].”
BackgroundMethod3-D shape recovery using distributed aspect matching
Dickinson, S., Pentland, A., & Rosenfeld, A. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121788 · Cited by 250
Case study of feature location using dependence graph
Chen, K., & Rajlich, V. (2002) · doi:10.1109/wpc.2000.852498 · Cited by 248
“All visited components and their neighbors constitute a search graph [4, 17].”
BackgroundA prolog technology theorem prover: Implementation by an extended prolog compiler
Stickel, M. E. (1988) · Journal of Automated Reasoning · doi:10.1007/bf00297245 · Cited by 247
“[ 15 ]. However, arbitrarily choosing a complete search strategy may result in the loss of much”
BackgroundMethodAnytime path planning and replanning in dynamic environments
van den Berg, J., Ferguson, D., & Kuffner, J. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1642056 · Cited by 241
“A popular technique in mobile robot navigation is to uniformly discretize the configuration space and then plan an optimal trajectory through this discretized representation using Dijkstra’s algorithm or A* [1].”
MethodAND/OR search spaces for graphical models
Dechter, R., & Mateescu, R. (2007) · Artificial Intelligence · doi:10.1016/j.artint.2006.11.003 · Cited by 241
Munos, R. (2014) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000038 · Cited by 241
“In the AI community, related works are the classical A* heuristic search [Nilsson, 1980] and the AO* variant from [Hansen and Zilberstein, 1999].”
Ramírez, M., & Geffner, H. (2009) · Cited by 244
Modeling and querying moving objects in networks
Güting, R. H., de Almeida, V. T., & Ding, Z. (2005) · The VLDB Journal · doi:10.1007/s00778-005-0152-x · Cited by 233
“Algorithms for computing shortest paths like Dijkstra’s algorithm or A∗ [39, 40] can be adapted in a straightforward way to use the modified adjacency list structure; they need to keep directed edges ordered by distance of their target nodes in the priority queue instead of nodes.”
MethodMetacognition in computation: A selected research review
Cox, M. T. (2005) · Artificial Intelligence · doi:10.1016/j.artint.2005.10.009 · Cited by 234
The organization of expert systems, a tutorial
Stefik, M., Aikins, J. S., Balzer, R. M., Benoit, J. W., Birnbaum, L. A., Hayes-Roth, F. A., & Sacerdoti, E. D. (1982) · Artificial Intelligence · doi:10.1016/0004-3702(82)90038-8 · Cited by 264
Planning as Heuristic Search: New Results
Bonet, B., & Geffner, H. (2000) · Lecture notes in computer science · doi:10.1007/10720246_28 · Cited by 233
“The fourth planner, hsp [BG98], was based on heuristic search [ Nil80 ,Pea83].”
MethodUnification: a multidisciplinary survey
Knight, K. K. (1989) · ACM Computing Surveys · doi:10.1145/62029.62030 · Cited by 232
Particle RRT for Path Planning with Uncertainty
Melchior, N. A., & Simmons, R. G. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363555 · Cited by 230
“This would produce a heuristic in the style of A [10] that would estimate the probability of successfully travelling from start to goal through any particular node.”
BackgroundA formal theory of plan recognition
Kautz, H. (1987) · UR Research (University of Rochester) · Cited by 233
Autonomous exploration and mapping of abandoned mines
Thrun, S., Thayer, S. C., Whittaker, W., Baker, C., Burgard, W., Ferguson, D., Hannel, D., Montemerlo, M., Morris, A., Omohundro, Z., et al. (2004) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2004.1371614 · Cited by 232
“Once the goal region has been determined, control is generated by applying the A* algorithm [ 34 ] in C-space.”
MethodFrom decision theory to decision aiding methodology
Tsoukiàs, A. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.02.039 · Cited by 252
“Indeed the problem of establishing a plan in order to “solve a problem” has been often viewed in artificial intelligence as the exploration of a tree structure where the nodes represent “states of the world” and the arcs represent transitions from one state to another ([109], [225], [263]).”
BackgroundData integration using similarity joins and a word-based information representation language
Cohen, W. W. (2000) · ACM Transactions on Information Systems · doi:10.1145/352595.352598 · Cited by 225
“This statement is a slight (and unsurprising) variant7 of the correctness property traditionally associated with A. search [Nilsson 1987], 7The principle differences are that A. usually is considered to minimize a sum of costs, rather than maximizing a score that is a product, and that A. usually…”
BackgroundMethodInfluentialFukada, H., & Takahashi, K. (1998) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19981101)33:2 · Cited by 313
Stewart, B. S., & White, C. C. (1991) · Journal of the ACM · doi:10.1145/115234.115368 · Cited by 228
Selection of Views to Materialize Under a Maintenance Cost Constraint
Gupta, H., & Mumick, I. S. (1999) · Lecture notes in computer science · doi:10.1007/3-540-49257-7_28 · Cited by 221
A subgraph isomorphism algorithm and its application to biochemical data
Bonnici, V., Giugno, R., Pulvirenti, A., Shasha, D. E., & Ferro, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s7-s13 · Cited by 220
“Other methods in the literatures do not rely on backtracking or CSP techniques, but rather apply heuristics based on probabilistic functions, explicit enumeration of matches, and so on [35-46].”
Robust reasoning: integrating rule-based and similarity-based reasoning
Sun, R. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(94)00028-y · Cited by 220
On the complexity of blocks-world planning
Gupta, N., & Nau, D. S. (1992) · Artificial Intelligence · doi:10.1016/0004-3702(92)90028-v · Cited by 220
“One of the primary roles of EBW in planning research was in the discovery and investigation of deleted-condition interactions such as creative destruction and Sussman’s anomaly [4, 15, 16, 18, 19, 20, 21], in which the plan for achieving one goal or subgoal deletes another goal or subgoal.”
BackgroundMethodAlanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0715-6 · Cited by 231
“John McCarthy, who coined the term in 1955, defined it as "the science and engineering of making intelligent machines" [6].”
BackgroundQuantitative deduction and its fixpoint theory
van Emden, M. H. (1986) · The Journal of Logic Programming · doi:10.1016/0743-1066(86)90003-8 · Cited by 217
A search algorithm for motion planning with six degrees of freedom
Donald, B. R. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90069-5 · Cited by 216
Map learning and high-speed navigation in RHINO
Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig, D., Hofmann, T., Krell, M. B., & Schmidt, T. T. (1998) · Cited by 217
Termination of logic programs: the never-ending story
Schreye, D. D., & Decorte, S. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90027-2 · Cited by 208
Complexity, decidability and undecidability results for domain-independent planning
Erol, K., Nau, D. S., & Subrahmanian, V. S. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(94)00080-k · Cited by 205
Dentronics: Towards robotics and artificial intelligence in dentistry
Grischke, J., Johannsmeier, L., Eich, L., Griga, L., & Haddadin, S. (2020) · Dental Materials · doi:10.1016/j.dental.2020.03.021 · Cited by 221
Organizational knowledge and the Intranet
Scott, J. E. (1998) · Decision Support Systems · doi:10.1016/s0167-9236(98)00032-3 · Cited by 204
Efficient Learning and Planning Within the Dyna Framework
Peng, J., & Williams, R. J. (1993) · Adaptive Behavior · doi:10.1177/105971239300100403 · Cited by 208
Differences between Novice and Expert Systems Analysts: What Do We Know and What Do We Do?
Schenk, K. D., Vitalari, N. P., & Davis, K. S. (1998) · Journal of Management Information Systems · doi:10.1080/07421222.1998.11518195 · Cited by 200
Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching
Garrido, S., Moreno, L. E., Abderrahim, M., & Martín, F. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.282649 · Cited by 200
“A∗ uses a heuristic to focus the search from a particular start location towards the goal, and thus produces a path from a single location to the goal very efficiently [37].”
MethodA Guide to Heuristic-based Path Planning
Ferguson, D., Likhachev, M. A., & Stentz, A. (2005) · Cited by 225
Conflict-free shortest-time bidirectional AGV routeing
KIM, C. W., & Tanchoco, J. M. A. (1991) · International Journal of Production Research · doi:10.1080/00207549108948090 · Cited by 201
Expertise and Error in Diagnostic Reasoning*
JOHNSON, P., Duran, A. S., Hassebrock, F., Moller, J. H., Prietula, M. J., Feltovich, P. J., & Swanson, D. B. (1981) · Cognitive Science · doi:10.1207/s15516709cog0503_3 · Cited by 211
Enterprise a Market-Like Task Scheduler for Distributed Computing Environments
Malone, T. W., Fikes, R. E., & Howard, M. T. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 200
Constraint Programming: In Pursuit of the Holy Grail
Barták, R. (1999) · Cited by 197
Footstep planning among obstacles for biped robots
Kuffner, J., Nishiwaki, K., Kagami, S., et al. (2002) · doi:10.1109/iros.2001.973406 · Cited by 196
“For an overview on dynamic programming, the reader is referred to any textbook on articial intelligence or game search trees (e.g. [4] or [ 10 ]).”
BackgroundSPROUT: A program for structure generation
Gillet, V. J., Johnson, A. P., Mata, P., Sike, S., & Williams, P. B. (1993) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00126441 · Cited by 201
“generation is well known as a combinatiorial problem [ 17 ] where attempts at finding solutions rapidly produce a large number of intermediate structures.”
BackgroundMethodPrinciples of Rule-Based Expert Systems
Buchanan, B. G., & Duda, R. O. (1983) · Advances in computers · doi:10.1016/s0065-2458(08)60129-1 · Cited by 195
“Our intent is to describe the general characteristics of the commonly used rule interpreters without becoming entangled ‘in . the detailed mcch&isms they employ; control stratcgics for more general problem solving systems are dcscr-ibcd in [Nilsson80].”
BackgroundA hierarchical strategy for path planning among moving obstacles (mobile robot)
Fujimura, K., & Samet, H. (1989) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.88018 · Cited by 192
Crime Mapping and the Crimestat Program
Levine, N. N. (2005) · Geographical Analysis · doi:10.1111/j.0016-7363.2005.00673.x · Cited by 190
“The A Ã shortest path algorithm is used to estimate the likely travel route taken (Nilsson 1980; Sedgewick 2002).”
MethodMulti-agent robot systems as distributed autonomous systems
Ota, J. (2006) · Advanced Engineering Informatics · doi:10.1016/j.aei.2005.06.002 · Cited by 194
Interpreting discourse: Coherence and the analysis of ethnographic interviews
Agar, M., & Hobbs, J. R. (1982) · Discourse Processes · doi:10.1080/01638538209544529 · Cited by 188
Challenging the Time Complexity of Exact Subgraph Isomorphism for Huge and Dense Graphs with VF3
Carletti, V., Foggia, P., Saggese, A., et al. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2696940 · Cited by 182
Deng, L. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2762725 · Cited by 205
“In the first rising wave of AI, starting in the 1960s and based on expert knowledge engineering, domain experts devised computer programs according to the knowledge about the (very narrow) application domains they have [3], [4].”
BackgroundA hierarchical strategy for path planning among moving obstacles
Fujimura, K., & Samet, H. (1989) · IEEE Journal on Robotics and Automation · Cited by 182
Indoor automation with many mobile robots
Caloud, P., Choi, W., Latombe, J.-C., Pape, C. L., & Yim, M. H. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1990.262370 · Cited by 181
GraphDB: Modeling and Querying Graphs in Databases
Güting, R. H. (1994) · Cited by 181
Web Service Composition as AI Planning { a Survey ⁄
Peer, J. (2005) · Cited by 179
Subrahmanian, V. S., Bonatti, P. A., Dix, J., Eiter, T. R., Kraus, S., Özcan, F., & Ross, R. B. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/3487.001.0001 · Cited by 177
Computing similarity in a reuse library system
Ostertag, E. J., Hendler, J., Díaz, R. P., et al. (1992) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/131736.131739 · Cited by 179
“The internal representation of the software library is constructed using a frame system with multiple inheritance [7], while the procedures used to find reuse candidates are based on A“-like search algorithms [ 16 ].”
MethodArtificial Intelligence in Education
Göksel, N., & Bozkurt, A. (2019) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-8431-5.ch014 · Cited by 193
Statistical Relational Artificial Intelligence
Raedt, L. D., Kersting, K., Natarajan, S., & Poole, D. (2016) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00692ed1v01y201601aim032 · Cited by 267
Automatically Generating Abstractions for Planning
Knoblock, C. A. (1993) · doi:10.21236/ada269772 · Cited by 176
Taking the leap between analytical chemistry and artificial intelligence: A tutorial review
Ayres, L. B., Gómez, F. J. V., Linton, J. R., Silva, M. F., & García, C. D. (2021) · Analytica Chimica Acta · doi:10.1016/j.aca.2021.338403 · Cited by 184
Parallel depth first search. Part II. Analysis
Kumar, V., & Rao, V. N. (1987) · International Journal of Parallel Programming · doi:10.1007/bf01389001 · Cited by 174
Liu, C.-L., Koga, M., & Fujisawa, H. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1046151 · Cited by 173
“Compared to best-first search or heuristic search [ 39 ], [40], the breadth-first search can overcome the bias to short strings [41] and obtain multiple solutions of string matching.”
BackgroundControl and Autonomy of Microrobots: Recent Progress and Perspective
Jiang, J., Yang, Z., Ferreira, A., & Zhang, L. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100279 · Cited by 178
Introduction to distributed algorithms
(1995) · Choice Reviews Online · doi:10.5860/choice.33-0987 · Cited by 174
Parallel depth first search. Part I. Implementation
Rao, V. N., & Kumar, V. (1987) · International Journal of Parallel Programming · doi:10.1007/bf01389000 · Cited by 172
“( 16 )] state-space search algorithm, as it runs in asymptotically optimal time for a wide class of search problems.”
BackgroundMethodInfluentialA history of AI and Law in 50 papers: 25 years of the international conference on AI and Law
Bench-Capon, T. J. M., Araszkiewicz, M., Ashley, K. D., Atkinson, K. M., Bex, F., Borges, F., Bourcier, D., Bourgine, P., Conrad, J. G., Francesconi, E., et al. (2012) · Artificial Intelligence and Law · doi:10.1007/s10506-012-9131-x · Cited by 183
Learning attack strategies from intrusion alerts
Ning, P., & Xu, D. (2003) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/948109.948137 · Cited by 170
An artificial intelligence approach to monitor student performance and devise preventive measures
Khan, I., Ahmad, A. R., Jabeur, N. H., & Mahdi, M. N. (2021) · Smart Learning Environments · doi:10.1186/s40561-021-00161-y · Cited by 170
ERNEST: a semantic network system for pattern understanding
Niemann, H. J., Sagerer, G., Schroder, S., et al. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.57683 · Cited by 167
Bayesian Logic ProgramsPreprint
Kersting, K., & Raedt, L. D. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0111058 · Cited by 166
Georgeff, M. (1988) · Elsevier eBooks · doi:10.1016/b978-0-934613-63-7.50022-x · Cited by 162
A formal model of diagnostic inference. I. Problem formulation and decomposition
Reggia, J. A., Nau, D. S., & Wang, P. Y. (1985) · Information Sciences · doi:10.1016/0020-0255(85)90015-5 · Cited by 162
A new general triangulation method for planar contours
Ganapathy, S., & Dennehy, T. G. (1982) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/800064.801264 · Cited by 160
A complete navigation system for goal acquisition in unknown environments
Stentz, A., & Hebert, M. (1995) · Autonomous Robots · doi:10.1007/bf00735431 · Cited by 159
Raedt, L. D., & Kersting, K. (2003) · ACM SIGKDD Explorations Newsletter · doi:10.1145/959242.959247 · Cited by 159
“… sound, they have also resulted in entirely new technologies for, and revolutionary novel products in computer vision, speech recognition, medical diagnostics, troubleshooting systems, etc. Overviews of and introductions to probabilistic learning can be found in[20;84;44;8;48;22; 64].”
BackgroundRobotic Systems Architectures and Programming
Kortenkamp, D. M., Simmons, R. G., & Brugali, D. (2016) · Springer handbooks · doi:10.1007/978-3-319-32552-1_12 · Cited by 157
A quantitative approach to logical inference
Hooker, J. (1988) · Decision Support Systems · doi:10.1016/0167-9236(88)90097-8 · Cited by 157
Evolutionary membrane computing: A comprehensive survey and new results
Zhang, G., Gheorghe, M., Pan, L., et al. (2014) · Information Sciences · doi:10.1016/j.ins.2014.04.007 · Cited by 156
“Also, their stochasticity improves the heuristic artificial intelligence [46,47] and machine-learning algorithms [44,66].”
BackgroundEfficient local search for very large-scale satisfiability problems
Gu, J. H. (1992) · ACM SIGART Bulletin · doi:10.1145/130836.130837 · Cited by 155
Manno, D., Cipriani, G., Ciulla, G., Dio, V. D., Guarino, S., & Brano, V. L. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.114315 · Cited by 163
Learning Maps for Indoor Mobile Robot Navigation.
Thrun, S., & Bücken, A. (1996) · Cited by 155
Parallel Search of Strongly Ordered Game Trees
Marsland, T. A., & Campbell, M. S. (1982) · ACM Computing Surveys · doi:10.1145/356893.356895 · Cited by 151
Path planning for a mobile robot
Alexopoulos, C., & Griffin, P. M. (1992) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.148404 · Cited by 149
“The path from s to t is found by using the A* algorithm in Nilsson [ 13 ].”
MethodTransition-based RRT for path planning in continuous cost spaces
Jaillet, L., Cortés, J., & Siméon, T. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650993 · Cited by 146
“Contrarily to sampling based techniques, these methods are however limited to low dimensional problems as they are based on graph search algorithms (eg.”
MethodAllying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes
Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R. T., & Martin, N. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107179 · Cited by 180
Perceptual Components for Context Aware Computing
Crowley, J. L., Coutaz, J., Rey, G., & Reignier, P. (2002) · Cited by 150
Modeling and Controlling Parallel Tasks in Droplet-Based Microfluidic Systems
Böhringer, K. F. (2006) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2005.855958 · Cited by 147
“, using the A∗ algorithm known from artificial intelligence programming [47].”
MethodAnytime synthetic projection: maximizing the probability of goal satisfaction
Drummond, M., & Bresina, J. L. (1990) · National Conference on Artificial Intelligence · Cited by 148
An overview of transaction logic
Bonner, A. J., & Kifer, M. (1994) · Theoretical Computer Science · doi:10.1016/0304-3975(94)90190-2 · Cited by 145
Kondo, K. (1991) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.88136 · Cited by 145
“Free-space cells concerning path planning are enumerated by a heuristic search technique based on a heuristic function [16], [ 17 ].”
Method
Metadata
- Authors
- Nilsson, N. J.
- Affiliations
- SRI International (US)
- Corresponding author
- Nilsson, Nils J.
- Venue
- Springer eBooks
- Publisher
- Springer Berlin Heidelberg
- Published
- 1982
- OpenAlex
- W2152475379
- Cited by
- 3,457
- References
- 0
- Citations by year
- 2026: 8 · 2025: 32 · 2024: 38 · 2023: 36 · 2022: 46 · 2021: 95 · 2020: 77 · 2019: 56 · 2018: 55 · 2017: 39 · 2016: 47 · 2015: 42 · 2014: 51 · 2013: 54 · 2012: 65
- FWCI
- 38.49
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Virtual Defense Library (Ministerio de Defensa) (submittedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Cumulative Index of Computer Aided Architectural Design (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- artificial intelligence, data structures, knowledge representation, natural language processing, program synthesis, robotics, machine vision, automated theorem proving
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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.
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