Pith. sign in

REVIEW 4 major objections 6 minor 57 references

Playing a Strategy Game with Knowledge-Based Reinforcement Learning

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Reinforcement learning can settle conflicts between expert rules and turn 11 FreeCiv strategies into one stronger player.

desk verdict A genuinely original hybrid idea—RL as the conflict-resolution layer for a multi-expert FreeCiv knowledge base—but the evaluation lacks the one control that would isolate RL's contribution, so the central claim is real and under-supported at the same time. read the letter →

arxiv 1908.05472 v1 pith:5X4WFDLQ submitted 2019-08-15 cs.AI

Viktor Voss , Liudmyla Nechepurenko , Rudi Schaefer , Steffen Bauer This is my paper · ORCID
classification cs.AI
keywords Knowledge-BasedReinforcementLearningconflictresolutionmulti-expertknowledgebaseFreeCivstrategygamesMonteCarlostate-spaceclusteringrule-basedsystems
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that a classic rule-based AI can be improved by using reinforcement learning as the arbiter of conflicting expert knowledge. Instead of asking knowledge engineers to hand-resolve disagreements among 11 human FreeCiv players, the authors merge all their rule sets into one multi-expert knowledge base and train a policy that picks which rule to fire when several match. The reported result is that the trained KB-RL agent completes full FreeCiv games, wins every contest game against each of the 11 expert knowledge bases, and wins in fewer turns on average (287) than the best single expert base (291). If this holds, multi-expert knowledge acquisition no longer has to discard or flatten diverse strategies; a learning system can preserve them and recombine them into a stronger player. The method matters beyond games because many real-world automation problems face the same issue of multiple experts giving different but individually reasonable procedures.

What carries the argument

The load-bearing object is the multi-expert knowledge base combined with the RL conflict resolver. A Knowledge Item is a single rule with three blocks (on, when, do) that encodes one atomic expert action; conflicts arise when more than one Knowledge Item matches the current situation. The resolver compresses FreeCiv's enormous state space with $k$-means clustering of 33 game features into 185 clusters, then maintains state-action values for each Knowledge Item per cluster using Monte-Carlo returns, with an $\epsilon$-greedy policy built from a normal distribution over the values. This machinery converts the knowledge-engineering problem of choosing one consistent rule set into an RL problem of learning which expert move is best in each cluster, and it is what lets the system combine strategies rather than average them.

What would settle it

Take the trained agent, without any further training, and run it on FreeCiv maps and starting positions randomly generated outside the five used in the paper; if it no longer wins the majority of games against the same expert knowledge bases, or its average turns to win rise above 291, the reported superiority is an artifact of the fixed evaluation setups.

Watch

Extended reading notes

Core claim

KB-RL is a rule-based knowledge system in which every expert strategy is encoded as Knowledge Items (rules with on/when/do blocks), and the inference engine applies abductive reasoning to fire matching rules. When several Knowledge Items conflict, a Monte-Carlo reinforcement learning policy decides among them: the game state is compressed by $k$-means clustering of 33 weighted features into 185 clusters, each Knowledge Item is treated as an action, and the episode return is the negative number of turns to finish, with losses penalised more heavily for being destroyed than for losing the space race. After 600 training episodes, the trained agent won all 110 games it played against expert knowledge bases in the five setups, averaged 287 turns to win versus 291 for the best expert base, and visibly composed a new strategy from fragments of different experts, such as limiting early city counts, expanding mid-game, and settling freely late.

Load-bearing premise

The load-bearing assumption is that the five fixed maps and fixed starting positions used in training and in the final contest are representative enough of FreeCiv's 'various game settings' that the trained agent's clean sweep over expert rule bases reflects general skill rather than memorisation of those specific setups.

Editorial extensions

If this is right

  • A multi-expert knowledge base no longer needs to be made internally consistent by hand; conflicting rules can be kept and ranked by learned value.
  • The KB-RL agent can play and complete the full FreeCiv game, not just isolated sub-tasks such as city building or defense.
  • Merging 11 expert rule sets produced an agent that won every game against each individual expert base, so combining strategies can beat the best single strategy.
  • The learned policy shortened average winning time to 287 turns versus the best expert's 291, and the paper reports improvement with more training games.
  • The approach gives a template for applying RL as a conflict-resolution layer in other rule-based systems where multiple experts provide alternative procedures.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural stress test is to evaluate the trained agent on maps and starting positions it never saw; if its win rate and turn advantage shrink, the reported dominance is tied to the five fixed setups rather than general FreeCiv skill.
  • The method should transfer to non-game rule-based domains with episodic success signals, but only where each episode can be replayed cheaply enough to collect hundreds of samples; without a simulator the 600-episode training cost becomes a practical barrier.
  • The average-turn difference of four turns is small, so the more convincing evidence of superiority is the perfect win record; re-running the contest with more than 10 games per matchup would test whether that record is robust.
  • Because the reward is purely turn-minimisation, the trained agent might sacrifice robustness or risk aversion; a reward that also penalises near-losses could produce a different and possibly safer strategy profile.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes Knowledge-Based Reinforcement Learning (KB-RL), a hybrid method that uses a rule-based knowledge system for acting and Monte Carlo reinforcement learning for resolving conflicts among rules originating from multiple human experts. The method is demonstrated on the full FreeCiv strategy game. Eleven expert knowledge bases (KI sets) are built from human players and made to compete in a 550-game tournament. A multi-expert knowledge base is then formed by combining all expert rules, and an RL agent learns to select among conflicting rules with a reward of -1 per turn. Section 5 reports that the trained agent won every contest game against the individual expert knowledge bases and won in 287 turns on average versus 291 turns for the best expert base (Tatamo). The paper interprets these results as evidence that RL can serve as an automated conflict-resolution mechanism that combines multiple expert strategies into a stronger player.

Significance. If the central claim is supported, the paper would offer a practically relevant demonstration that reinforcement learning can be used to integrate conflicting knowledge from multiple experts, turning rule conflicts into a learned policy rather than requiring manual knowledge engineering. The scale of the experiment is a genuine strength: 550 tournament games, 1,100 recorded game histories, a publicly released game-history dataset (DOI in reference [56]), and full evaluation against 11 expert rule sets. The contribution is, however, not yet convincingly isolated: the evaluation lacks a baseline of the multi-expert knowledge base without RL, uses the same five fixed maps for training and testing, and reports aggregate averages without error bars. The turn-count improvement is also directly encoded in the reward function, so it cannot by itself validate the learned policy. The win-rate result is promising but needs an appropriate control to be attributed to RL.

major comments (4)
  1. [Section 4.6 / Section 5] The central claim that RL improves gameplay is not supported by an adequate control. The trained agent uses the multi-expert knowledge base formed by taking the union of all 11 expert KI sets, while each opponent in Section 5 uses only its own single-expert KI set. To attribute the observed wins and the 287-turn average to the learned conflict-resolution policy, the authors must also run the same multi-expert knowledge base with a non-learning conflict-resolution rule such as random selection, first-match priority, or a fixed priority order. As the experiment stands, the superiority of the trained agent may be due entirely to the fact that its rule base contains all experts' rules rather than to what RL contributes.
  2. [Section 4.3 / Section 4.6 / Section 5] The evaluation is performed on the same five fixed maps and starting positions that were used during training. Section 4.3 states that maps and starting positions were deliberately kept fixed, and Section 4.6 trains and Section 5 tests on those same setups. The abstract's phrase 'various game settings' is therefore an overstatement, and the all-wins result may reflect memorization of these specific maps. Moreover, Section 5 reports only average turns (287 vs. 291) with no standard deviations, per-agent distributions, or significance tests; with ten games per pairing, the four-turn gap is within plausible noise. The paper should report the full distribution of results and, ideally, evaluate on held-out maps or random starts.
  3. [Section 4.6] The reward function returns -1 for each turn played, so reducing the number of turns to win is exactly the objective being optimized. The observed shortening of the game length is therefore an expected consequence of the training signal and cannot by itself serve as independent evidence that KB-RL 'improves the gameplay.' The independent evidence is the win rate against expert rule sets, but that evidence is obtained on the same maps used in training and lacks the multi-expert baseline described above. The paper should separate these two claims and treat the win-rate result, not the turn count, as the primary validation of the RL component.
  4. [Section 4.6] The state representation itself is fitted to the tournament data: the 33 features are selected by their correlation with won/lost outcomes, the feature weights are derived from that analysis, and the number of clusters k=185 is chosen by experimentation on the same data. Because the final evaluation uses the same maps and the same game conditions, there is a risk of overfitting the state space to the tournament environment. A validation procedure that separates state-representation selection from evaluation would strengthen the claim that the learned policy generalizes.
minor comments (6)
  1. [Abstract / Section 1] The abstract uses 'multiple exerts' where 'multiple experts' is meant; the same typo appears in Section 1.
  2. [Figure 4] The caption labels subfigure b) as Chaos and c) as Medium Islands, but Section 4.3 refers to 'Medium Islands' as Figure 4 b) and 'Chaos' as Figure 4 c); the labels should be made consistent.
  3. [Eq. (1)] In Equation (1), the symbol G_s is used for both the state return and a component of the variance, and the expected-return term is not explicitly defined; please introduce distinct notation for the mean and variance.
  4. [Table 1 / Figure 7] Expert names are used inconsistently: 'Alex' in Table 1 appears as 'Alex DrKaffee' in Figure 7, and several other names are shortened in captions. Please unify the naming.
  5. [References] The RDF references contain typographical errors: '20014' should be '2014' and '2919' should be '2019'.
  6. [Section 4.6] The description of the policy is not fully precise: the paper states that a Normal distribution is used to sample actions, but it is not clear whether the learned policy remains stochastic during the post-training contest or whether epsilon-exploration is switched off at evaluation time; please specify the exact evaluation policy.

Circularity Check

1 steps flagged · score 4.0 of 10

The turns-to-win improvement is the RL reward itself, making that headline result partly self-referential; the win-rate result provides independent content.

  1. self definitional [Section 4.6 (reward function definition) and Section 5 (results)]
    "Therefore, the reward function for the RL agent was chosen to be based on the number of turns the game lasted, with the objective of minimizing the game rounds to win. The defined reward function returned -1 for each turn played in the game... Consequently, for each winning episode, its return was defined as G = -N, where N is the last turn of the game. ... In alignment with the outlined goal, the KB-RL system learned to win the game on average in fewer turns in comparison to the agents with the expert knowledge bases."

    The headline result 'the system improves the gameplay by shortening the number of rounds that it takes to win' is the exact quantity that the reward function is defined to minimize. The reward is G = -N for a winning episode, with N the number of turns, so reporting lower average turns after training is a restatement of the optimization objective rather than an independent prediction. This makes the turns-to-win claim self-referential by construction. The other reported result, that the trained agent won every contest game against the expert knowledge bases, is not directly identical to the reward function and therefore provides partially independent evidence for the method.

full rationale

The paper's central derivation is not circular in the sense of importing a conclusion from self-citations or defining the method in terms of the outcome. The KB-RL architecture, multi-expert knowledge base, state clustering, Monte-Carlo policy, and the contest against expert rule sets are all independently described and implemented. However, the most prominent quantitative improvement, fewer turns to win, is literally the reward function being optimized: the return for a winning episode is defined as G = -N, where N is the number of turns. Evaluating the trained agent on average turns to win therefore reports the objective itself, not a derived consequence. This is partial circularity because the win-rate result and the qualitative claim that the agent recombines expert strategies are not encoded as directly in the reward. The lack of an ablation comparing the multi-expert knowledge base without RL is a missing control but not a circular step. Self-citations such as the Open Graph of IT ontology and the FreeCiv game dataset are not load-bearing for the main claim, so they do not raise the score further. Overall, the turns-to-win claim is partly self-fulfilling, but the central claim retains independent content from the win-rate evidence.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central empirical claim rests on a large number of hand-set engineering choices: cluster count, feature weights, reward constants, exploration rate, discount factor, and training length. The knowledge rules are assumed to be faithful encodings of expert strategies, and the fixed-map evaluation is assumed to generalize. These are domain assumptions, not standard mathematical axioms.

free parameters (7)
  • Number of state clusters k = 185
    Chosen by experimenting with cluster counts on the 386895-sample tournament dataset; no held-out validation or objective criterion is reported (Section 4.6).
  • Weights for 33 clustering features = not specified
    Weights were assigned based on correlation with won/lost outcome and adjusted for 'best clustering accuracy'; exact values are not given (Section 4.6).
  • Reward constants = -1 per turn for wins; -2N for space losses; 1000-N for destruction losses
    Hand-set to favor faster peaceful wins; the 1000 offset and double penalty are arbitrary design choices (Section 4.6).
  • Epsilon exploration probability = not specified
    The paper states an epsilon-greedy policy is used but does not give the value or schedule (Section 3.12).
  • Training length = 600 episodes
    Training stopped after 600 games because each game took 8-12 hours; no convergence criterion is reported (Section 4.6).
  • Policy selection threshold L = mu_max - sigma_max
    The limit for computing action probabilities is an arbitrary algorithmic choice (Section 4.6).
  • Discount factor gamma = 1
    Returns are undiscounted, making total return equal to negative game length by design (Section 4.6).
assumptions (5)
  • domain assumption The 33 selected features and the 185 k-means clusters capture all decision-relevant state for conflict resolution.
    Section 4.6 reduces the game to 185 clusters; if the abstraction discards information, the learned state-action values are not valid for the true state.
  • domain assumption The rules elicited from 11 players via protocol analysis faithfully represent those players' strategies.
    Section 4.4 describes encoding expert play into KIs but provides no verification that the encoded rules reproduce expert behavior.
  • domain assumption The client auto-saved file contains sufficient game state for the agent to make correct decisions.
    Section 4.2 uses only client auto-saved files, so hidden server-side state is unavailable to the agent.
  • standard math Monte Carlo return averaging with the hand-set reward converges to a policy that genuinely minimizes turns to win.
    The paper relies on standard MC convergence and on the reward being an adequate proxy for gameplay quality (Sections 3.12 and 4.6).
  • domain assumption The five fixed maps and starting positions are representative of 'various game settings' for the final evaluation.
    Section 4.3 fixes maps to enforce characteristics; Section 4.6 evaluates on the same setups, so generalization depends on this assumption.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Playing a Strategy Game with Knowledge-Based Reinforcement Learning." pith.science (2026). https://pith.science/paper/5X4WFDLQ

@misc{pith2026190805472,
  author       = {Pith},
  title        = {Pith review of: Playing a Strategy Game with Knowledge-Based Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5X4WFDLQ}},
  note         = {Machine review of arXiv:1908.05472}
}
read the original abstract

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a method that combines a knowledge-based approach and a reinforcement learning (RL) technique into one method for intelligent problem solving. The proposed approach focuses on multi-expert knowledge acquisition, with the reinforcement learning being applied as a conflict resolution strategy aimed at integrating the knowledge of multiple exerts into one knowledge base. The article describes the KB-RL approach in detail and applies the reported method to one of the most challenging problems of current Artificial Intelligence (AI) research, namely playing a strategy game. The results show that the KB-RL system is able to play and complete the full FreeCiv game, and to win against the computer players in various game settings. Moreover, with more games played, the system improves the gameplay by shortening the number of rounds that it takes to win the game. Overall, the reported experiment supports the idea that, based on human knowledge and empowered by reinforcement learning, the KB-RL system can deliver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 55 canonical work pages

  1. [56]

    FreeCiv games played by Knowledge-based Reinforcement Learning, 2019

    Viktor Voss and Liudmyla Nechepurenko. FreeCiv games played by Knowledge-based Reinforcement Learning, 2019. DOI: 10.5281/zenodo.3266624

    work page doi:10.5281/zenodo.3266624 2019

  2. [1]

    URL: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

    AlphaStar: Mastering the Real-Time Strategy Game StarCraft II. URL: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

    work page

  3. [2]

    URL: https://github.com/arago/OGIT/wiki (visited on 05/08/2019)

    Open Graph of IT Ontology. URL: https://github.com/arago/OGIT/wiki (visited on 05/08/2019)

    work page 2019

  4. [3]

    URL: https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/ (visited on 05/08/2019)

    RDF 1.1 Concepts and Abstract Syntax, 20014. URL: https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/ (visited on 05/08/2019)

    work page 2014

  5. [4]

    URL: https://www.w3.org/TR/2014/REC-turtle-20140225/ (vis- ited on 05/08/2919)

    RDF 1.1 Turtle, 20014. URL: https://www.w3.org/TR/2014/REC-turtle-20140225/ (vis- ited on 05/08/2919)

    work page 2014

  6. [5]

    Knowledgebased systems: a reevaluation

    Mohd Syazwan Abdullah, Chris Kimble, Ian Benest, and Richard Paige. Knowledgebased systems: a reevaluation. Journal of Knowledge Management , 10(3):127–142, 2006. DOI: 10.1108/13673270610670902

    work page doi:10.1108/13673270610670902 2006

  7. [6]

    Aha, Matthew Molineaux, and Marc Ponsen

    David W. Aha, Matthew Molineaux, and Marc Ponsen. Learning to win: Case-based plan selection in a real-time strategy game. In Proceedings of the 6th International Confer- ence on Case-Based Reasoning Research and Development, ICCBR’05, pages 5–20, Berlin, Heidelberg, 2005. Springer-Verlag

    work page 2005

  8. [7]

    Conflict resolution in a knowledge-based system using multiple attribute decision-making

    Byeong Seok Ahn and Sang Hyun Choi. Conflict resolution in a knowledge-based system using multiple attribute decision-making. Expert Syst. Appl. , 36(9):11552–11558, 2009

    work page 2009

Show all 57 references
  1. [8]

    Knowledge-Based Systems

    Rajendra Akerkar and Priti Sajja. Knowledge-Based Systems. Jones and Bartlett Pub- lishers, Inc., USA, 1st edition, 2009

    2009

  2. [9]

    Felix Arnold, Bryan Horvat, and Albert M. Sacks. Freeciv learner : A machine learning project utilizing genetic algorithms. 2007

    2007

  3. [10]

    Empirical study on knowledge based systems

    Gabriela Avram. Empirical study on knowledge based systems. The Electronic Journal of Information Systems Evaluation , 8:11–20, 2005

    2005

  4. [11]

    S. R. K. Branavan, David Silver, and Regina Barzilay. Learning to win by read- ing manuals in a monte-carlo framework. CoRR, abs/1401.5390, 2014. URL: http://arxiv.org/abs/1401.5390

    2014 arXiv

  5. [12]

    The beta policy for continuous control reinforcement learning

    Po-Wei Chou. The beta policy for continuous control reinforcement learning. Master’s thesis, Carnegie Mellon University, Pittsburgh PA, 06 2017

    2017

  6. [13]

    Representing knowledge as arguments: Applying expert system technology to judgemental problem-solving

    Peter Clark. Representing knowledge as arguments: Applying expert system technology to judgemental problem-solving. Research and Development in Expert Systems VII, pages 147–159, 1990

    1990

  7. [14]

    Graham Curtis and David P. Cobham. Business information systems: analysis, design, and practice / Graham Curtis and David Cobham. Financial Times/Prentice Hall, 2002. Playing a Strategy Game with Knowledge-Based Reinforcement Learning 25

    2002

  8. [15]

    Resolving conflicts in clinical guidelines using argu- mentation

    Kristijonas Cyras and Tiago Oliveira. Resolving conflicts in clinical guidelines using argu- mentation. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’19, pages 1731–1739, Richland, SC, 2019. International Foundation for ...

    2019

  9. [16]

    Ontoshare - an ontology-based knowledge shar- ing system for virtual communities of practice

    John Davies, Alistair Duke, and York Sure. Ontoshare - an ontology-based knowledge shar- ing system for virtual communities of practice. Journal of Universal Computer Science , 10(3):262–283, 03 2004

    2004

  10. [17]

    Knowledge acquisition for explainable, multi-expert, knowledge-based design systems

    Rose Dieng, Alain Giboin, Paul-Andr Tourtier, and Olivier Corby. Knowledge acquisition for explainable, multi-expert, knowledge-based design systems. In EKAW, pages 298–317, 05 1992. DOI: 10.1007/3-540-55546-3 47

    1992 doi

  11. [18]

    Abduction

    Igor Douven. Abduction. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, summer 2017 edition, 2017

    2017

  12. [19]

    Handling conflict between domain descriptions with computer- supported negotiation

    Steve Easterbrook. Handling conflict between domain descriptions with computer- supported negotiation. Knowledge Acquisition, 3:255–289, 09 1991. DOI: 10.1016/1042- 8143(91)90007-A

    1991 doi

  13. [20]

    Towards a definition of knowledge graphs

    Lisa Ehrlinger and Wolfram W¨ oß. Towards a definition of knowledge graphs. InSEMAN- TiCS (Posters, Demos, SuCCESS) , 2016

    2016

  14. [21]

    Agents and multi-agent systems: A short introduction for power engi- neers, 05 2006

    Mevludin Glavic. Agents and multi-agent systems: A short introduction for power engi- neers, 05 2006

    2006

  15. [22]

    Thomas R. Gruber. A translation approach to portable ontology specifications. KNOWL- EDGE ACQUISITION, 5:199–220, 1993

    1993

  16. [23]

    Ontologies and knowledge bases: Towards a terminological clarification

    Nicola Guarino and Pierdaniele Giaretta. Ontologies and knowledge bases: Towards a terminological clarification. In Towards very Large Knowledge bases: Knowledge Building and Knowledge sharing , pages 25–32. IOS Press, 1995

    1995

  17. [24]

    Hinrichs and K

    T. Hinrichs and K. Forbus. Toward higher-order qualitative representations. InProceedings of QR 2012 , 2012

    2012

  18. [25]

    Analogical learning in a turn-based strategy game

    Thomas R Hinrichs and Kenneth D Forbus. Analogical learning in a turn-based strategy game. IJCAI International Joint Conference on Artificial Intelligence , pages 853–858, 12 2007

    2007

  19. [26]

    Software metrics: good, bad and missing

    Capers Jones. Software metrics: good, bad and missing. Computer, 27:98 – 100, 10 1994. DOI: 10.1109/2.312055

    1994 doi

  20. [27]

    Knowledge organization and structural credit assignment

    Joshua Jones and Ashok Goel. Knowledge organization and structural credit assignment. In In Proc. IJCAI-05 Workshop on Reasoning, Representation and Learning in Computer Games, Edinburgh, UK, 08 2005

    2005

  21. [28]

    Joshua Jones and Ashok K. Goel. Metareasoning for adaptation of classification knowledge. In AAMAS, 2009

    2009

  22. [29]

    Joshua Jones, Chris Parnin, Avik Sinharoy, Spencer Rugaber, and Ashok K. Goel. Adapt- ing game-playing agents to game requirements. In Proc. Fifth AAAI Conference on Ar- tificial Intelligence and Interactive Digital Entertainment (AIIDE-09) , pages 148–153, 2009

    2009

  23. [30]

    Temporal knowledge acquisition from multiple ex- perts

    Helen Kaikova and Vagan Terziyan. Temporal knowledge acquisition from multiple ex- perts. In Proceedings of NGITS97, Neve Ilan, June 30 - July 3 , pages 44–55, 1997

    1997

  24. [31]

    Khalil, M

    Khaled M. Khalil, M. Abdel-Aziz, Taymour T. Nazmy, and Abdel-Badeeh M. Salem. Intelligent techniques for resolving conflicts of knowledge in multi-agent decision support systems. ArXiv, abs/1401.4381, 2014

    2014 arXiv

  25. [32]

    Experimenting statecharts for multiple experts knowl- edge elicitation in agriculture

    Bertrand L´ eger and Olivier Naud. Experimenting statecharts for multiple experts knowl- edge elicitation in agriculture. Expert Syst. Appl. , 36(8):11296–11303, 2009

    2009

  26. [33]

    Semantic networks

    Fritz Lehmann. Semantic networks. Computers and Mathematics with Applications , 23 (2-5):1–50, 1992

    1992

  27. [34]

    Shaw and Brian Gaines

    Mildred L.G. Shaw and Brian Gaines. Comparing conceptual structures: Consensus, con- flict, correspondence and contrast. Knowledge Acquisition , 1:341–363, 08 2000. DOI: 10.1016/S1042-8143(89)80010-X

    doi

  28. [35]

    Expert systems

    Peter Lucas. Expert systems. In Joost Nico Kok, editor, Encyclopedia of Life Support Systems (EOLSS), pages 328–356. Eolss Publishers, Paris, 2009

    2009

  29. [36]

    Knowledge acquisition from multiple experts

    A¨ ıda Valls Mateu and Vicen Torra i Revent´ os. Knowledge acquisition from multiple experts. 2004

    2004

  30. [37]

    Knowledge acquisition for multiple site, related domain expert systems: Delphi process and application

    Diane Matteo Hamilton and Steven Breslawski. Knowledge acquisition for multiple site, related domain expert systems: Delphi process and application. Expert Systems with Applications, 11:377–389, 12 1996. DOI: 10.1016/S0957-4174(96)00052-8

    1996 doi

  31. [38]

    Expert systems inference = modeling conflicts

    Tim Menzies. Expert systems inference = modeling conflicts. In In Proceedings of the ECAI 96 workshop on Modelling Conflicts in AI , 07 1996. 26 Viktor Voss et al

    1996

  32. [39]

    Sanjay Mittal and Clive L. Dym. Knowledge acquisition from multiple experts. AI Mag- azine, 6(2):32–36, 1985

    1985

  33. [40]

    Playing atari with deep reinforcement learning

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. ArXiv, abs/1312.5602, 2013

    2013 arXiv

  34. [41]

    Aha, and Marc J

    Matthew Molineaux, David W. Aha, and Marc J. V. Ponsen. Defeating novel opponents in a real-time strategy game. In ICCBR, 2012

    2012

  35. [42]

    Playing Fair: A Look at Competition in Gaming, 03 2009

    Mark Newheiser. Playing Fair: A Look at Competition in Gaming, 03 2009. URL: http://strangehorizons.com/non-fiction/articles/playing-fair-a-look-at-competition- in-gaming/

    2009

  36. [43]

    Noy and Deborah L

    Natalya F. Noy and Deborah L. Mcguinness. Ontology development 101: A guide to creating your first ontology. Technical report, 2001

    2001

  37. [44]

    Knowledge acquisition from multiple experts: An empirical study

    Daniel O’Leary. Knowledge acquisition from multiple experts: An empirical study. Man- agement Science, 44:1049–1058, 08 1998. DOI: 10.1287/mnsc.44.8.1049

    1998 doi

  38. [45]

    Expert systems and knowledge-based engineering (1984-1991)

    Jo Ann Oravec. Expert systems and knowledge-based engineering (1984-1991). Interna- tional Journal of Designs for Learning , 7, 11 2014. DOI: 10.14434/ijdl.v5i2.12891

    1984 doi

  39. [46]

    Pakiarajah, P

    V. Pakiarajah, P. Crowther, and J. Hartnett. Conflict resolution techniques for expert systems used to classify remotely sensed satellite images, 2000. URL: http://www.geocomputation.org/2000/GC025/Gc025.htm

    2000

  40. [47]

    Terziyan

    Seppo Puuronen and Vagan Y. Terziyan. Knowledge acquisition from multiple experts based on semantics of concepts. In Proceedings of the 11th European Workshop on Knowl- edge Acquisition, Modeling and Management , EKAW ’99, pages 259–273, London, UK, UK, 1999. Springer-Verlag

    1999

  41. [48]

    Eliciting expertise

    N R Shadbolt. Eliciting expertise. In John R Wilson and Nigel E Corlett, editors, Evalu- ation of Human Work . Taylor and Francis Ltd, 2005

    2005

  42. [49]

    Mastering the game of go with deep neural networks and tree search

    David Silver, Aja Huang, Christopher Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Mad...

    2016

  43. [50]

    Lillicrap, Karen Simonyan, and Demis Hassabis

    David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timo- thy P. Lillicrap, Karen Simonyan, and Demis Hassabis. Mastering chess and shogi by self-play with a general rein...

    2017 arXiv

  44. [51]

    Semantic networks

    John F Sowa. Semantic networks. In Lynn Nadel, editor, Encyclopedia of Cognitive Science. Wiley, 2006

    2006

  45. [52]

    P. H. Speel, Anders Schreiber, Wouter R. van Joolingen, Gertjan van Heijst, and Gertjan Beijer. Conceptual models for knowledge-based systems. In Journal of Computer Science and Technology - JCST, New York, 2001

    2001

  46. [53]

    Sutton and Andrew G

    Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction . The MIT Press, second edition, 2018. URL: http://incompleteideas.net/book/the-book- 2nd.html

    2018

  47. [54]

    State-space dimensionality reduction in markov decision processes

    O Sykora. State-space dimensionality reduction in markov decision processes. In Proceed- ings of Contributed Papers , WDS’08, page 165170, 2008

    2008

  48. [55]

    Using model-based reflec- tion to guide reinforcement learning

    Patrick Ulam, Ashok Goel, Joshua Jones, and William Murdock. Using model-based reflec- tion to guide reinforcement learning. In In Fourth AAAI Conference on AI in Interactive Digital Entertainment, 2008

    2008

  49. [57]

    Optimization in strategy games : Using genetic algorithms to optimize city development in freeciv

    Ian Watson, Damir Azhar, Y Tang Chuyang, Wei Pan, and Gary Chen. Optimization in strategy games : Using genetic algorithms to optimize city development in freeciv. 2009. DOI: 10.1.1.567.7035

    2009

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.