The Causal Calculator (CCalc) is a system for representing commonsense knowledge about action and change. It implements a fragment of the causal logic described in the paper ”Nonmonotonic causal theories” by Enrico Giunchiglia, Joohyung Lee, Vladimir Lifschitz, Norman McCain and Hudson Turner (Artificial Intelligence, Vol. 153, 2004, pp. 49-104). The original version of CCalc was part of Norman McCain’s dissertation, Causality in commonsense reasoning about actions (University of Texas, 1997). Now the system is being maintained by Texas Action Group at Austin. The semantics of the language of CCalc is related to default logic and logic programming. Computationally, CCalc uses ideas of satisfiability planning. (A related system, Cplus2ASP from Arizona State University, processes CCalc input using answer set solvers instead of SAT solvers.)

References in zbMATH (referenced in 79 articles , 1 standard article )

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  1. Artikis, Alexander; Makris, Evangelos; Paliouras, Georgios: A probabilistic interval-based event calculus for activity recognition (2021)
  2. Czelakowski, Janusz: Performability of actions (2021)
  3. Wang, Yi; Lee, Joohyung: Elaboration tolerant representation of Markov decision process via decision-theoretic extension of probabilistic action language (p\mathcalBC+) (2021)
  4. Bartholomew, Michael; Lee, Joohyung: First-order stable model semantics with intensional functions (2019)
  5. Białek, Łukasz; Dunin-Kęplicz, Barbara; Szałas, Andrzej: A paraconsistent approach to actions in informationally complex environments (2019)
  6. Dimopoulos, Yannis; Gebser, Martin; Lühne, Patrick; Romero, Javier; Schaub, Torsten: plasp 3: towards effective ASP planning (2019)
  7. Wang, Yi; Zhang, Shiqi; Lee, Joohyung: Bridging commonsense reasoning and probabilistic planning via a probabilistic action language (2019)
  8. Lee, Joohyung; Wang, Yi: A probabilistic extension of action language (\mathcalBC+) (2018)
  9. Lee, Joohyung; Loney, Nikhil; Meng, Yunsong: Representing hybrid automata by action language modulo theories (2017)
  10. Yalciner, Ibrahim Faruk; Nouman, Ahmed; Patoglu, Volkan; Erdem, Esra: Hybrid conditional planning using answer set programming (2017)
  11. De Giacomo, Giuseppe; Lespérance, Yves; Patrizi, Fabio: Bounded situation calculus action theories (2016)
  12. Fandinno, Jorge: Deriving conclusions from non-monotonic cause-effect relations (2016)
  13. Inclezan, Daniela; Gelfond, Michael: Modular action language (\mathcalALM) (2016)
  14. Lent, Jeremy; Thomason, Richmond H.: Action models for conditionals (2015)
  15. Skarlatidis, Anastasios; Paliouras, Georgios; Artikis, Alexander; Vouros, George A.: Probabilistic event calculus for event recognition (2015)
  16. Ji, Jianmin; Chen, Xiaoping: A weighted causal theory for acquiring and utilizing open knowledge (2014)
  17. Babb, Joseph; Lee, Joohyung: Cplus2ASP: computing action language (\mathcalC+) in answer set programming (2013)
  18. Gelfond, Michael; Inclezan, Daniela: Some properties of system descriptions of (\mathcalAL_d) (2013)
  19. Giordano, Laura; Martelli, Alberto; Theseider Dupré, Daniele: Reasoning about actions with Temporal Answer Sets (2013)
  20. Lifschitz, Vladimir; Yang, Fangkai: Functional completion (2013)

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