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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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Optimistic Planning for Markov Decision Processes
Buşoniu, L., & Munos, R. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 32
IBM Watson Application as FAQ Assistant about Moodle
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“a) It is a system of cognitive computing: inserted in the universe of Artificial Intelligence [7].”
BackgroundIs Artificial Intelligence Ready for Standardization?
Zielke, T. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-56441-4_19 · Cited by 38
Computer assisted evaluation of retinal vessels tortuosity in Fabry disease
Sodi, A., Guarducci, M., Vauthier, L., et al. (2012) · Acta Ophthalmologica · doi:10.1111/j.1755-3768.2012.02576.x · Cited by 32
“It aims to the minimization of a function that describes how much the current solution is far away from what is considered the desired solution (whose properties are suitably encoded into the mathematical function) (Nilsson 1980).”
BackgroundPractical introduction to expert systems
Gallacher, J. (1989) · Microprocessors and Microsystems · doi:10.1016/0141-9331(89)90034-3 · Cited by 32
A two-layered subgoal based mobile robot navigation algorithm with vision system and IR sensors
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An efficient algorithm for searching implicit AND/OR graphs with cycles
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“Among the several AO∗-like algorithms proposed in the literature [2,7,17–19], we have chosen to extend CF to the case of cyclic graphs, because it was shown in [17] to have the best performance.”
MethodApplication of a Heuristic Search Method for Generation of Fuel Reload Configurations
Galperin, A., & Nissan, E. (1988) · Nuclear Science and Engineering · doi:10.13182/nse88-a23563 · Cited by 32
AI-based animation for interactive storytelling
Cavazza, M., Charles, F., & Mead, S. J. (2002) · doi:10.1109/ca.2001.982384 · Cited by 32
“We use the AO* algorithm [Nilsson, 1980] [Pearl, 1984] [Knight and Rich, 1991] to search the AND/OR graph.”
Car-like robots and moving obstacles
Fraichard, T., & Scheuer, A. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1994.351009 · Cited by 32
Finding legal reordering transformations using mappings
Kelly, W., & Pugh, W. (1995) · Lecture notes in computer science · doi:10.1007/bfb0025874 · Cited by 32
Plan abstraction based on operator generalization
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Hypertext and structured object representation
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Design aspects of an advanced model-oriented DSS for scheduling problems in civil engineering
Bartusch, M., Möhring, R. H., & Radermacher, F. J. (1989) · Decision Support Systems · doi:10.1016/0167-9236(89)90013-4 · Cited by 32
On completeness of narrowing strategies
Echahed, R. (2005) · Springer eBooks · doi:10.1007/bfb0026098 · Cited by 32
A Path Planning for Line Segmentation of Handwritten Documents
Surinta, O., Holtkamp, M., Karabaa, F., et al. (2014) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2014.37 · Cited by 31
“This paper proposes a line segmentation method based on the A∗ path-planning algorithm [9].”
MethodManteiga, M., Carricajo, I., Rodrı́guez, A., et al. (2009) · The Astronomical Journal · doi:10.1088/0004-6256/137/2/3245 · Cited by 31
Artificial Intelligence in Smart Cities
Rjab, A. B., & Mellouli, S. (2019) · International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3326365.3326400 · Cited by 31
“Nilsson (2014) defined in [26] this technology as a subpart of computer science, concerned with how to give computers the sophistication to act intelligently [26].”
BackgroundOptimized GUI Generation for Small Screens
Raneburger, D., Popp, R., Kavaldjian, S., et al. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-14562-9_6 · Cited by 30
Automatic Generation of Detailed Mechanisms
Blurock, E. S., Battin‐Leclerc, F., Faravelli, T., et al. (2013) · Green energy and technology · doi:10.1007/978-1-4471-5307-8_3 · Cited by 31
VANETsim: An open source simulator for security and privacy concepts in VANETs
Tomandl, A., Herrmann, D., Fuchs, K.-P., et al. (2014) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2014.6903733 · Cited by 31
Inductive learning of search control rules for planning
Leckie, C. A., & Ingrid Zukerman, A. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00018-6 · Cited by 31
Supporting advanced interaction capabilities on a mobile robot with a flexible control system
Kleinehagenbrock, M., Fritsch, J., & Sagerer, G. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389982 · Cited by 31
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Heuristic search theory: survey of recent results
Pearl, J. (1981) · International Joint Conference on Artificial Intelligence · Cited by 31
Hiemstra, D. (1998) · Cited by 31
Derived preconditions and their use in program synthesis
Smith, D. R. (2005) · Springer eBooks · doi:10.1007/bfb0000059 · Cited by 31
“If $ is A then o is the unifying composition of el and 92' denoted uc(e 1 , 92 ) [7].”
BackgroundThe role of memory and concepts in learning
Epstein, S. L. (1992) · Minds and Machines · doi:10.1007/bf02454222 · Cited by 30
“N-N/Tree uses knowledge about the minimax algorithm for search control and how to apply it three-ply deep ( Nilsson, 1980 ).”
MethodObstacle Prediction-based Dynamic Path Planning for a Mobile Robot
Wu, Z., & -, L. F. (2012) · International Journal of Advancements in Computing Technology · doi:10.4156/ijact.vol4.issue3.16 · Cited by 30
“[13] Nilsson N J, “Principles of Artificial Intelligence”, Springer, Germany, 1980 [14] Yugeng Xi, Chungang Zhang, “Rolling Path Planning of Mobile Robot in a Kind of Dynamic Uncertain Environment”, ACTA Automatica SINICA, Science Press, vol.”
BackgroundMethodGrundlagen der Künstlichen Intelligenz und des Maschinellen Lernens
Buxmann, P., & Schmidt, H. (2018) · Künstliche Intelligenz · doi:10.1007/978-3-662-57568-0_1 · Cited by 32
GeOasis: A knowledge-based geo-referenced tourist assistant
Santiago, F. J. M., López, F. J. A., Montejo‐Ráez, A., et al. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.04.080 · Cited by 30
On randomization and discovery
Rubin, S. H. (2006) · Information Sciences · doi:10.1016/j.ins.2006.06.001 · Cited by 30
Baruwa, O. T., & Piera, M. À. (2014) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-014-6065-3 · Cited by 30
“Classical heuristic search algorithm like A ∗ [19] does not return the optimal solution until it terminates.”
BackgroundMemory-Bounded A* Graph Search
Zhou, R., & Hansen, E. A. (2002) · Cited by 30
Motion planning for humanoid robots in environments modeled by vision
Nakhaei, A., & Lamiraux, F. (2008) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2008.4755945 · Cited by 31
“Several approaches exist for solving such a common problem of motion planning. [ 10 , 7, 5, 6].”
BackgroundBest-first AND/OR search for graphical models
Marinescu, R., & Dechter, R. (2007) · National Conference on Artificial Intelligence · Cited by 30
PHI : a logic-based tool for intelligent help systems
Bauer, M., Biundo, S., Dengler, D., Koehler, J., & Paul, G. (1992) · doi:10.22028/d291-24865 · Cited by 30
“To generate these hypotheses , the planner works from second principles by reusing formerly generated plans.”
MethodGeneralized case-based reasoning system for portfolio management
Chi, R. T., Chen, M., & Kiang, M. Y. (1993) · Expert Systems with Applications · doi:10.1016/0957-4174(93)90019-3 · Cited by 30
String taxonomy using learning automata
Oommen, B. J., & Croix, E. V. D. S. (1997) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.558849 · Cited by 30
“Furthermore, unlike heuristic methods [7], [ 30 ] which can merely impose a user’s criterion for closeness between two strings, we can now generalize a closeness criterion to extrapolate whether a string belongs in a potential subdictionary.”
MethodParallel processing of recursive queries in distributed architectures
Hulin, G. (1989) · Very Large Data Bases · Cited by 30
Roy, U. (1996) · Computers & Industrial Engineering · doi:10.1016/0360-8352(96)80914-4 · Cited by 30
Exact solutions for constrained two-dimensional cutting problems
Daza, V. P., de Alvarenga, A. G., & de Diego, J. (1995) · European Journal of Operational Research · doi:10.1016/0377-2217(95)00028-o · Cited by 30
A Predicate-Transition Net Model for Multiple Agent Planning
Murata, T., Nelson, P. C., & Yim, J. (1991) · Information Sciences · doi:10.1016/0020-0255(91)90087-b · Cited by 30
A Standalone Wire Database for Routing and Tracing in Xilinx Virtex, Virtex-E, and Virtex-II FPGAs
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Constraint-based interactive assembly planning
Jones, R. E., Wilson, R. H., & Calton, T. L. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1997.614251 · Cited by 30
“Our assembly constraint filters are an instance of generate-and-test, a standard paradigm in artificial intelligence [8] as well as in assembly planning [3, 41. Attaching special-purpose methods to some filters is also a well known efficiency technique [ 8 ], although the methods we use to accomplish this are novel.”
MethodLearning explanation-based search control rules for partial order planning
Katukam, S., & Kambhampati, S. (1994) · Cited by 30
PRA*: a memory-limited heuristic search procedure for the Connection Machine
Evett, M. P., Hendler, J., Mahanti, A., et al. (2002) · Symposium on the Frontiers of Massively Parallel Computation (Frontiers) · doi:10.1109/fmpc.1990.89450 · Cited by 30
Priam, an interactive program for chosing among multiple attribute alternatives
Lévine, P., & Pomerol, J. (1986) · European Journal of Operational Research · doi:10.1016/0377-2217(86)90091-3 · Cited by 30
The CDP: A Unifying Formulation for Heuristic Search, Dynamic Programming, and Branch-and-Bound
Kumar, V., & Kanal, L. N. (1988) · Springer eBooks · doi:10.1007/978-1-4613-8788-6_1 · Cited by 30
“, [23], [11], [12], [13], [5], [27], [28]).”
BackgroundAbstract interaction tools: a language for user interface management systems
van den Bos, J. (1988) · ACM Transactions on Programming Languages and Systems · doi:10.1145/42190.42191 · Cited by 30
“The latter action comprises applications, (implicit) input parsing, echoing, error correction, and so on. To date, this is still the prevailing way of handling I/O, even in standard proposals such as Core [4] and the Graphics Kernel System (GKS) [ 12 ].”
BackgroundMethodKnowledge based routing and sequencing for discrete part production
Ben‐Arieh, D., & Moodie, C. L. (1987) · Journal of Manufacturing Systems · doi:10.1016/0278-6125(87)90005-7 · Cited by 30
L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning
Niewola, A., & Podsędkowski, L. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0748-6 · Cited by 30
Study on mobile robot navigation techniques
Ibrahim, M. Y., & Fernandes, A. (2005) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2004.1490288 · Cited by 29
Combining ordered best-first search with branch and bound for exact BDD minimization
Ebendt, R., Gunther, W., & Drechsler, R. (2005) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2005.852053 · Cited by 29
A tristate optical logic system
Basuray, A., Mukhopadhyay, S., Ghosh, H. K., et al. (1991) · Optics Communications · doi:10.1016/0030-4018(91)90388-t · Cited by 29
Partial-order reduction for general state exploring algorithms
Bošnački, D., Leue, S., & Lafuente, A. L. (2008) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-008-0093-y · Cited by 29
The Coherence of Incoherent Discourse
Hobbs, J. R., & Agar, M. (1985) · Journal of Language and Social Psychology · doi:10.1177/0261927x8543004 · Cited by 29
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Fixpoint iteration with subsumption in deductive databases
K�stler, G., Kießling, W., Th�ne, H., et al. (1995) · Journal of Intelligent Information Systems · doi:10.1007/bf00961871 · Cited by 29
“We perform bidirectional heuristic search on this graph by running two A*-algorithms (Nilsson, 1980), one starting at s and heading forward for t and a second one starting at t and heading backward for s as shown by Figure 1.”
BackgroundMethodMulti-contributor causal structures for planning: a formalization and evaluation
Kambhampati, S. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90083-3 · Cited by 29
“In the following we tighten this formulation by imposing some additional restrictions to derive special classes of multi-contributor causal structures with interesting properties.”
MethodAn algebraic and predicate logic approach to representation and reasoning in machine design
Kannapan, S. M., & Marshek, K. M. (1990) · Mechanism and Machine Theory · doi:10.1016/0094-114x(90)90033-g · Cited by 29
Three-dimensional techniques and artificial intelligence in thallium-201 cardiac imaging
Depuey, E. G., Garcia, E. V., & Ezquerra, N. F. (1989) · American Journal of Roentgenology · doi:10.2214/ajr.152.6.1161 · Cited by 29
A game-theoretic control approach for job shops in the presence of disruptions
Leon, V. J., Wu, S. D., & Storer, R. H. (1994) · International Journal of Production Research · doi:10.1080/00207549408957010 · Cited by 29
Deterministic POMDPs RevisitedPreprint
Bonet, B. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2659 · Cited by 29
“An AND/OR graph is a tuple G = (V1 ∪ V2, E, T, n0, c) where V1 and V2 are finite sets of AND and OR nodes, E ⊆ (V1∪V 2)\T×(V1∪V2) is a subset of directed edges between nodes, T ⊆ V2 is subset of terminal nodes, n0 ∈ V1 ∪ V2 is the root node, and c : E ∪ T → R∗ is a cost function over edges and terminal nodes [26].”
BackgroundMethodConstraint-driven generation of model structures
Rozenblit, J. W., & Huang, Y. (1987) · Winter Simulation Conference · doi:10.1145/318371.318671 · Cited by 29
SYGRAF: implementing logic programs in a database style
Kifer, M., & Lozinskii, E. L. (1988) · IEEE Transactions on Software Engineering · doi:10.1109/32.42735 · Cited by 29
Search Strategies for Automatic Web Service Composition
Milanović, N., & Malek, M. (2006) · International Journal of Web Services Research · doi:10.4018/jwsr.2006040101 · Cited by 28
“Two well known heuristic approaches that fit our problem description are A* (Hart, 1968; Hart, 1972) and AO* (Martelli, 1973; Martelli, 1978; Nilsson 1980 } search algorithms.”
MethodDCG + GTE: Dynamic Courseware Generation with teaching expertise
Vassileva, J. (1998) · Instructional Science · doi:10.1023/a:1003015532585 · Cited by 28
Feature Selection for Graph-Based Image Classifiers
Saux, B. L., & Bunke, H. (2005) · Lecture notes in computer science · doi:10.1007/11492542_19 · Cited by 28
“To compute the graph edit distance, we use the A ∗ algorithm [ 12 ] as described for graph matching in [13].”
MethodPernestål, A., Nyberg, M., & Warnquist, H. (2011) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2011.02.018 · Cited by 28
Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images
Gao, Z., Guo, W., Liu, X., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109997 · Cited by 28
“At last, the border of the calcified plaque is detected by the graph searching method algorithm [28] with cost function formatted by MIC and the image gradient.”
MethodSearching for designs: Paradigm and practice
Woodbury, R. F. (1991) · Building and Environment · doi:10.1016/0360-1323(91)90040-i · Cited by 28
“These are lucidly explained in numerous publications [13-16].”
BackgroundA Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance
Dezfoulian, S. H., Wu, D., & Ahmad, I. S. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-33926-4_3 · Cited by 28
“The existing path planning methods include A* algorithm [75], potential fields [2], and methods using intelligent control technique such as neural networks and neuro-fuzzy.”
MethodPath planning of unmanned aerial vehicles with terrestrial wireless network tracking
Bekhti, M., Abdennebi, M., Achir, N., et al. (2016) · Wireless Days (WD) · doi:10.1109/wd.2016.7461521 · Cited by 28
“The two most popular techniques are deterministic, heuristic-based algorithms [8], [9], [10] and probabilistic, randomized algorithms [11] and [12].”
MethodNabizadeh, A. H., Jorge, A., & Leal, J. P. (2017) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3099023.3099035 · Cited by 27
Solving the Generalized Machine Assignment Problem in Group Technology
Cheng, C. H., Goh, C., & Lee, A. (1996) · Journal of the Operational Research Society · doi:10.1057/jors.1996.99 · Cited by 28
Hybrid modes in cooperating distributed grammar systems: internal versus external hybridization
Fernau, H., Holzer, M., & Freund, R. (2001) · Theoretical Computer Science · doi:10.1016/s0304-3975(00)00022-0 · Cited by 28
A close look at task assignment in distributed systems
Ramakrishnan, S., Cho, I.-H., & Dunning, L. A. (1991) · doi:10.1109/infcom.1991.147588 · Cited by 28
Parallel logical inference and energy minimization
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A: an efficient near admissible heuristic search algorithm
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A probabilistic analysis of prepositional STRIPS planning
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The geometrical representation of path planning problems
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An approach to continuous speech recognition based on layered self-adjusting decoding graph
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An Algorithm for a Constraint Optimization Problem in Mobile Ad-hoc Networks
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“The A* algorithm, also introduced and detailed in [13], is a type of search algorithm which is used in finding one of the shortest paths.”
MethodSearch Algorithms for m Best Solutions for Graphical Models
Dechter, R., Flerova, N., & Marinescu, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8405 · Cited by 27
“The task is to find the least cost solution path from n0 to a goal (Nillson 1980), where the cost of a solution path is the sum of the weights or the product of the weights on its arcs.”
BackgroundInfluentialChatbots — an organisation’s friend or foe?
Carter, E., & Knol, C. (2019) · Research in Hospitality Management · doi:10.1080/22243534.2019.1689700 · Cited by 27
The process of research investigations in artificial intelligence-a unified view
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Balancing meals using fuzzy arithmetic and heuristic search algorithms
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The Intelligent Travel Assistant
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Code generation based on formal BURS theory and heuristic search
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“Search techniques are used extensively in artificial intelligence [34, 30] where data is dynamically generated.”
MethodA/sup */ search: an efficient and flexible approach to materialized view selection
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Theoretical Analysis of Heuristic Search Methods for Online POMDPs.
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Constraint Programming - What is behind?
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On the performance of the CREL system
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Automating operating procedure synthesis for batch processes
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The Intentional planning system: ItPlanS
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Low order polynomial bounds on the expected performance of local improvement algorithms
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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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