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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
Citations
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Items 901–1,000 of 1,002
Bayoudh, S., Mouchère, H., Miclet, L., & Anquetil, É. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_49 · Cited by 22
“To generate the k best sequences regarding to ADS, we use the general search algorithmA [12] used to find the k shortest paths in a graph [13], with a heuristic value set to 0.”
MethodDepth-first heuristic search for the job shop scheduling problem
Mencía, C., Sierra, M. R., & Varela, R. (2012) · Annals of Operations Research · doi:10.1007/s10479-012-1296-x · Cited by 22
“In this case, we have the A ∗ algorithm proposed by Hart et al. (1968) and very well described by Nilsson (1980) and Pearl (1984).”
MethodClinical decision support systems in myocardial perfusion imaging
Garcia, E. V., Klein, J. L., & Taylor, A. T. (2014) · Journal of Nuclear Cardiology · doi:10.1007/s12350-014-9857-9 · Cited by 22
Flexible real-time mobile robotic architecture based on behavioural models
Hassan, H., Simó, J. E., & Crespo, A. (2001) · Engineering Applications of Artificial Intelligence · doi:10.1016/s0952-1976(01)00029-x · Cited by 22
Optimistic planning for continuous-action deterministic systems
Buşoniu, L., Daniels, A., Munos, R., et al. (2013) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2013.6614991 · Cited by 22
Human Information Interaction, Artificial Intelligence, and Errors
Russell, S., Moskowitz, I. S., & Raglin, A. J. (2017) · Springer eBooks · doi:10.1007/978-3-319-59719-5_4 · Cited by 27
“Advances in AI allow increasingly difficult reasoning problems to be addressed (Bond and Gasser, 2014; Nilsson, 2014) and advanced cognitive activities, such as those that exist in naturally in the human brain represent some of the most difficult reasoning problems to artificially re-create.”
BackgroundOdili, J. B., Kahar, M. N. M., Noraziah, A., & Kamarulzaman, S. F. (2017) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881417705969 · Cited by 22
“In fact, robots are only as “intelligent” as the artificial intelligence techniques/programs that control them.(5) One of the biggest contributions of artificial intelligence (AI), therefore, is the creation of “smarter” robots.”
BackgroundPaquet, E. (2006) · Cited by 22
Experimental and theoretical artificial intelligence
Dietrich, E., & Fields, C. (1989) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528138908953690 · Cited by 22
REAL-TIME OPTIMAL-ROUTE COMPUTATION: A HEURISTIC APPROACH
Karimi, H. A. (1996) · Journal of Intelligent Transportation Systems · doi:10.1080/10248079608903712 · Cited by 22
Enhancing Gameplay: Challenges for Artificial Intelligence in Digital Games
Charles, D. K. (2003) · Cited by 22
An upper bound for the speedup of parallel best-bound branch-and-bound algorithms
Quinn, M. J., & Deo, N. (1986) · BIT Numerical Mathematics · doi:10.1007/bf01939360 · Cited by 22
Latchup-aware placement and parasitic-bounded routing of custom analog cells
Basaran, B., Rutenbar, R. A., & Carley, L. R. (1993) · doi:10.5555/259794.259862 · Cited by 22
A high-performance exact method for the resource-constrained project scheduling problem
Zamani, M. R. (2001) · Computers & Operations Research · doi:10.1016/s0305-0548(00)00048-4 · Cited by 22
Accelerating partial order planners by improving plan and goal choices
Schubert, L. K., & Gerevini, A. (2002) · doi:10.1109/tai.1995.479839 · Cited by 22
“Thus we will prefer to pursue a plan p’ that seems closer to being complete to a plan p further from completion, even though the overall complexity estimate for p’ may be greater than for p [ 19 ] (pages 87-88).”
BackgroundGo-Moku and Threat-Space Search
Allis, L. V., van den Herik, H. J., & Huntjens, M. P. H. (1993) · Cited by 22
Biologically-Inspired Computing Approaches To Cognitive Systems: a partial tour of the literature
Cliff, D. (2003) · Cited by 22
Minimizing response times in real time planning and search
Shekhar, S., & Dutta, S. (1989) · International Joint Conference on Artificial Intelligence · Cited by 22
Problem solving, recall, and isomorphic transfer among third-grade and sixth-grade children
Gholson, B., Eymard, L. A., Morgan, D. T., & Kamhi, A. G. (1987) · Journal of Experimental Child Psychology · doi:10.1016/0022-0965(87)90061-0 · Cited by 22
Parsing word graphs using a linguistic grammar and a statistical language model
Schmid, L. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1994.389723 · Cited by 22
“The A* algorithm [ 2 ] can be used to solve this graph search problem, because it enumerates the paths between the two vertices according to descending scores.”
MethodArtificial intelligence in landscape architecture: a survey
Xing, Y., Gan, W., & Chen, Q. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02536-w · Cited by 32
“The concept of artificial intelligence (AI) was first proposed by John McCarthy, at the Dartmouth conference in 1956 [120].”
Toward Visual Grounding: A Survey
Xiao, L., Yang, X., Lan, X., Wang, Y., & Xu, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3630635 · Cited by 80
“By extracting common technical details, this survey encompasses the representative work in each subtopic over the past decade.”
Optimal Privacy-Aware Path in Hippocratic Databases
Li, M., Sun, X., Wang, H., & Zhang, Y. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00887-0_39 · Cited by 21
“In particular, this notation does not distinguish if a sub-purpose is derived by AND or OR decomposition [19].”
BackgroundA Survey on Smooth Path Generation Techniques for Nonholonomic Autonomous Vehicle Systems
Sharma, O., Sahoo, N. C., & Puhan, N. B. (2019) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2019.8926946 · Cited by 21
“[22] N.”
A NEW METHOD FOR INFLUENCE DIAGRAM EVALUATION
Qi, R., & Poole, D. (1995) · Computational Intelligence · doi:10.1111/j.1467-8640.1995.tb00046.x · Cited by 21
An efficient solution to autonomous path planning by Approximate Cell Decomposition
Arney, T. (2007) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2007.4544785 · Cited by 21
“Graph searching is a well defined problem with a number of solutions that can be found in many artificial intelligence texts [14].”
BackgroundMethodA Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge
Todd, M., McArthur, S. D. J., McDonald, J. R., et al. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.900645 · Cited by 21
“EBG uses a reduced form of goal regression [ 32 ] to generalize the explanation structure and gives a clear specification of the input and output of the expression to be derived.”
MethodPlanning for Markov Decision Processes with Sparse Stochasticity
Likhachev, M. A., Thrun, S., & Gordon, G. J. (2004) · Cited by 21
Robotic manipulation for the shoe-packaging process
Gracia, L., Pérez-Vidal, C., Mronga, D., de Paco, J. M., Azorín, J. M., & Fernández, J. D. G. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0212-6 · Cited by 21
Distributed problem solving and real-time mechanisms in robot architectures
Lane, D. M., & McFadzean, A. G. (1994) · Engineering Applications of Artificial Intelligence · doi:10.1016/0952-1976(94)90016-7 · Cited by 21
AI and Robotics; Flexibility and Integration
Mowforth, P., & Bratko, I. (1987) · Robotica · doi:10.1017/s0263574700015058 · Cited by 21
Putra, A. E., Rukun, K., Irfan, D., et al. (2020) · Asian Social Science and Humanities Research Journal (ASHREJ) · doi:10.37698/ashrej.v2i1.22 · Cited by 21
“So that it can be defined artificial intelligence is a process in which mechanical equipment can implement events using intelligence or thinking like humans (Nilsson, 2014; Russell & Norvig, 2002).”
BackgroundBayesian network models for generation of crisis management training scenarios
Grois, E., Hsu, W. H., Voloshin, M., et al. (1998) · Cited by 21
Expert systems approach to decision support
Ligęza, A. (1988) · European Journal of Operational Research · doi:10.1016/0377-2217(88)90284-6 · Cited by 21
Shaw, M. J. (1989) · International Journal of Flexible Manufacturing Systems · doi:10.1007/bf00222707 · Cited by 21
EZ: A system for automatic prototyping of Z specifications
Doma, V., & Nicholl, R. A. (1991) · Lecture notes in computer science · doi:10.1007/3-540-54834-3_13 · Cited by 21
“Two kinds of control strategies for problem solving search systems are tentative, or noncommittal, and irrevocable [ 12 ].”
BackgroundLearning dominance relations in combined search problems
Yu, C. F., & Wah, B. W.-S. (1988) · IEEE Transactions on Software Engineering · doi:10.1109/32.7626 · Cited by 21
Expert systems and their applications in production and operations management
Gupta, Y. P., & Chin, D. C. W. (1989) · Computers & Operations Research · doi:10.1016/0305-0548(89)90042-7 · Cited by 20
Artificial intelligence methods in quantitative electroencephalogram analysis
Jagannathan, V., Bourne, J. R., Jansen, B. M. P., et al. (1982) · Computer Programs in Biomedicine · doi:10.1016/0010-468x(82)90009-5 · Cited by 21
Expert systems for image processing-knowledge-based composition of image analysis processes
Matsuyama, T. (2003) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1988.28187 · Cited by 21
Ben‐Arieh, D., Moodie, C. L., & Chu, C.-N. (1988) · IEEE Journal on Robotics and Automation · doi:10.1109/56.771 · Cited by 21
Lessons learned from a second generation assembly planning system
Ames, A. L., Calton, T. L., Jones, R. E., et al. (2002) · IEEE International Symposium on Assembly and Task Planning (ISATP) · doi:10.1109/isatp.1995.518749 · Cited by 21
On the NP-ardness of blocks world
Chenoweth, S. V. (1991) · National Conference on Artificial Intelligence · Cited by 21
Automated learning of load-balancing strategies for a distributed computer system
Mehra, P. (1993) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 21
Generating rules from examples
Arbab, B., & Michie, D. (1985) · Cited by 21
Kibler, D. F., & Morris, P. H. (1981) · International Joint Conference on Artificial Intelligence · Cited by 21
Hybrid Approaches to Pattern Recognition
Fu, K. S. (1982) · Springer eBooks · doi:10.1007/978-94-009-7772-3_11 · Cited by 21
Kambhampati, S. (1993) · International Joint Conference on Artificial Intelligence · Cited by 21
The Relationship Between AND/OR Search and Variable EliminationPreprint
Mateescu, R., & Dechter, R. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1407 · Cited by 21
“A well known Best-first version of AND/OR spaces is the AO* algorithm [14].”
MethodQuotient Space Based Problem Solving: A Theoretical Foundation of Granular Computing
Zhang, L., & Zhang, B. (2014) · Cited by 20
Autonomous Robot of the University of Karlsruhe
Rembold, U., & Dillmann, R. (1987) · Springer eBooks · doi:10.1007/978-3-642-87387-4_17 · Cited by 21
On the role of artificial intelligence in music research
Camurri, A. (1990) · Interface · doi:10.1080/09298219008570568 · Cited by 21
Placement of mmWave Base Stations for Serving Urban Drone Corridors
Singh, S., Bhattacherjee, U., Öztürk, E., et al. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-spring51267.2021.9448923 · Cited by 20
“The A* search algorithm is quite popular in solving such problems [10], leveraging initial information of the environment and heuristics to calculate the shortest trajectory [11].”
MethodBorello, E. S., Peter, C., Panini, F., et al. (2021) · Energy · doi:10.1016/j.energy.2021.122151 · Cited by 20
Best-first utility-guided search
Ruml, W., & Minh, B. Q. (2007) · International Joint Conference on Artificial Intelligence · Cited by 20
Siagian, C., Chang, C. K., & Itti, L. (2014) · Journal of Field Robotics · doi:10.1002/rob.21505 · Cited by 20
Iterative-Deepening Search with On-Line Tree Size Prediction
Burns, E., & Ruml, W. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34413-8_1 · Cited by 20
Pruning by dominance in best-first search for the job shop Scheduling problem with total flow time
Sierra, M. R., & Varela, R. (2008) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0167-4 · Cited by 20
“For best-first search we have chosen the A∗ Nilsson’s algorithm (Nilsson 1980).”
MethodPath planning for cognitive vehicles using risk maps
Schroder, J., Gindele, T., Jagszent, D., et al. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621290 · Cited by 20
“An incremental graph search algorithm was introduced by Nilsson in 1980 and later known as A* algorithm [9].”
MethodSharma, R. S., Shukla, S., Karki, H., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793911 · Cited by 20
“The general approach for motion planning of nonholonomic robots is a two level process, where a path finding algorithm like A* [6] searches for a collision free path in the configuration space.”
BackgroundArtificial Intelligence Methods
Artiba, A., Emelyanov, V. V., & Iassinovski, S. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5709-8_4 · Cited by 20
Algorithms for the Satisfiability Problem
Gu, J., Purdom, P. W., Franco, J. V., et al. (2018) · Cited by 20
Cavellucci, C. (1997) · International Transactions in Operational Research · doi:10.1016/s0969-6016(96)00032-9 · Cited by 20
Managing search complexity in linguistic geometry
Stilman, B. (1997) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.650058 · Cited by 20
“[30] N. J. Nilsson, Principles of Artificial Intelligence.”
BackgroundMethodInfluentialSearch Heuristics for Box Decomposition Methods
Ratschan, S. (2002) · Journal of Global Optimization · doi:10.1023/a:1016246616462 · Cited by 20
Hierarchical A∗ based path planning — a case study
Autere, A. (2002) · Knowledge-Based Systems · doi:10.1016/s0950-7051(01)00121-6 · Cited by 20
A sequential decoder for linear block codes with a variable bias-term metric
Sorokine, V., & Kschischang, F. R. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.651072 · Cited by 20
“Thus sequential decoding with the Fano metric is an instance of Algorithm A in the terminology of [19].”
MethodHamidzadeh, B., & Lilja, D. J. (1994) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.1994.179 · Cited by 20
“The static version of a class of these algorithms is the heuristic search algorithm A* [ 9 ].”
BackgroundA logic programming approach to fault diagnosis in distribution ring networks
Wong, K. P., & Tsang, C. P. (1988) · Electric Power Systems Research · doi:10.1016/0378-7796(88)90012-0 · Cited by 20
Sheet metal bending operation planning: Using virtual node generation to improve search efficiency
Gupta, S. K. (1999) · Journal of Manufacturing Systems · doi:10.1016/s0278-6125(99)80017-x · Cited by 20
Process supervision by means of a hybrid model
Lunze, J., Nixdorf, B., & Richter, H. (2001) · Journal of Process Control · doi:10.1016/s0959-1524(99)00067-0 · Cited by 20
The optimality of A* revisited
Dechter, R., & Pearl, J. (1983) · National Conference on Artificial Intelligence · Cited by 20
Extending conventional planning techniques to handle actions with context-dependent effects
Pednault, E. (1988) · National Conference on Artificial Intelligence · Cited by 20
Amalgamating knowledge bases, III: Algorithms, data structures and query processing
Adalı, S., & Subrahmanian, V. S. (1996) · The Journal of Logic Programming · doi:10.1016/0743-1066(95)00148-4 · Cited by 20
Science, philosophy, and interpretation
Dennett, D. C. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058878 · Cited by 19
Lycan, W. G. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0005874x · Cited by 19
Research Review: Application of Expert Systems in the Sciences
Durkin, J. R. (1990) · The Knowledge Bank (The Ohio State University) · Cited by 19
Melekhin, V. B. (2019) · Journal of Computer and Systems Sciences International · doi:10.1134/s1064230719050101 · Cited by 19
Visual GUI Testing: Automating High-level Software Testing in Industrial Practice
Alégroth, E. (2015) · Chalmers Research (Chalmers University of Technology) · Cited by 19
Expert systems for medical applications
Holman, J. G., & COOKSON, M. J. (1987) · Journal of Medical Engineering & Technology · doi:10.3109/03091908709008986 · Cited by 19
A Comparison of Three Maximum Common Subgraph Algorithms on a Large Database of Labeled Graphs
Conte, D., Guidobaldi, C., & Sansone, C. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45028-9_12 · Cited by 19
“The algorithm proposed by Balas and Yu can be suitably described through a State Space Representation [8].”
BackgroundMethodInfluentialDesign Pattern Mining Using State Space Representation of Graph Matching
Gupta, M., Rao, R. S., Pande, A., et al. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-17857-3_32 · Cited by 19
Neuro-Activity-Based Dynamic Path Planner for 3-D Rough Terrain
Saputra, A. A., Toda, Y., Botzheim, J., et al. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2711013 · Cited by 19
Partial-Order Reduction for General State Exploring Algorithms
Bošnački, D., Leue, S., & Lafuente, A. L. (2006) · Lecture notes in computer science · doi:10.1007/11691617_16 · Cited by 19
Artificial Intelligence and Automation
Nau, D. S. (2009) · Springer handbooks · doi:10.1007/978-3-540-78831-7_14 · Cited by 19
Mastrogiovanni, F., & Sgorbissa, A. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.09.028 · Cited by 19
“of a subgraph rooted in a node ni starting from the cost of the subgraphs rooted in its immediate successors nj ∈ S(ni): see [37] for details.”
BackgroundMethodReactive route selection from pre-calculated trajectories – application to micro-UAV path planning
Hall, J., & Anderson, D. (2011) · The Aeronautical Journal · doi:10.1017/s0001924000006321 · Cited by 19
Pattern rewriting framework for event processing optimization
Rabinovich, E., Etzion, O., & Gal, A. (2011) · doi:10.1145/2002259.2002277 · Cited by 19
A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle
Finkelstein, L., & Markovitch, S. (1998) · Journal of Artificial Intelligence Research · doi:10.1613/jair.484 · Cited by 19
“{ 10-cannibals: The cannibals and missionaries problem (Nilsson, 1980).”
BackgroundA Survey of Intelligent Scheduling Systems
Brown, D. E., Marin, J. A., & Scherer, W. T. (1995) · Springer eBooks · doi:10.1007/978-1-4615-2263-8_1 · Cited by 19
Multiobjective Heuristic Search in AND/OR Graphs
Dasgupta, P., Chakrabarti, P. P., & DeSarkar, S. C. (1996) · Journal of Algorithms · doi:10.1006/jagm.1996.0015 · Cited by 19
Information Acquisition Policies for Resource Allocation Among Multiple Agents
Moore, J. C., Rao, H. R., Whinston, A. B., et al. (1997) · Information Systems Research · doi:10.1287/isre.8.2.151 · Cited by 19
Formulating concepts according to purpose
Kedar-Cabelli, S. T. (1987) · National Conference on Artificial Intelligence · Cited by 19
HORNLOG: A graph-based interpreter for general Horn clauses
Gallier, J. H., & Raatz, S. (1987) · The Journal of Logic Programming · doi:10.1016/0743-1066(87)90015-x · Cited by 19
Problem solving by expert systems in analytical chemistry
Klaessens, J., & Kateman, G. (1987) · Fresenius Zeitschrift für Analytische Chemie · doi:10.1007/bf00474064 · Cited by 19
“A thorough discussion of this subject has been given by Nilsson [ 11 ].”
BackgroundWhat you always wanted to know about clause graph resolution
Eisinger, N. (1986) · Lecture notes in computer science · doi:10.1007/3-540-16780-3_100 · Cited by 19
Learning diagnostic policies from examples by systematic searchPreprint
Bayer‐Zubek, V. (2004) · arXiv (Cornell University) · doi:10.5555/1036843.1036847 · Cited by 19
Review of Expert Systems for the Management Science Practitioner
Fordyce, K. J., Norden, P. V., & Sullivan, G. A. (1987) · INFORMS Journal on Applied Analytics · doi:10.1287/inte.17.2.64 · Cited by 19
Parallel state-space search for a first solution with consistent linear speedups
Kal�, L. V., & Saletore, V. A. (1990) · International Journal of Parallel Programming · doi:10.1007/bf01379360 · Cited by 19
“We used the Manhattan distance heuristic (the h(n) function) (39) to estimate the cost of a particular board configuration from the goal configuration.”
MethodEfficient Convergent Maximum Likelihood Decoding on Tail-Biting TrellisesPreprint
Shankar, P., Kumar, P., Sasidharan, K., et al. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0601023 · Cited by 19
“The A algorithm [23] has been used for maximum likelihood soft decision decoding on conventional trellises for block codes [10], [9], [19], [11], [12], [26].”
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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