Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.
EricssonKA. An introduction to Cambridge handbook of expertise and expert performance: its development, organization, and content. In: EricssonKACharnessNFeltovichPJ, et al. (eds) The Cambridge handbook of expertise and expert performance, chapter 1. Cambridge: Cambridge University Press, 2006, pp. 3–20.
2.
HannayJEKikkeY. Structured crisis training with mixed-reality simulations. In: Proceedings of the 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM), ISCRAM Association, Valencia, 19–22 May 2019, pp. 1310–1319.
3.
PapineauD. Philosophy of science. In: BunninNTsui-JamesEP (eds) The Blackwell companion to philosophy. Hoboken, NJ: Blackwell Publishing, 1996, pp. 286–317.
4.
ZeiglerBPPraehoferHKimTG. Theory of modeling and simulation. 2nd ed.Cambridge, MA: Academic Press, 2000.
5.
BaralCGelfondM. Reasoning agents in dynamic domains. In: MinkerJ (ed.) Logic-based artificial intelligence. Boston, MA: Springer, 2000, pp. 257–279.
6.
GelfondMKahlY. Knowledge representation, reasoning, and the design of intelligent agents: the answer-set programming approach. Cambridge: Cambridge University Press, 2014.
7.
BaralCGelfondGPontelliE, et al. An action language for multi-agent domains. Artif Intell2022; 302: 103601.
8.
LifschitzV. Answer set programming. Heidelberg: Springer, 2019.
9.
GiunchigliaELeeJLifschitzV, et al. Nonmonotonic causal theories. Artif Intell2004; 153: 49–104.
10.
MakinsonD. General patterns in nonmonotonic reasoning. In: GabbayDHoggerCRobinsonJ (eds) Handbook of logic in Artificial intelligence and logic programming, Vol. III. Oxford: Clarendon Press, 1994, pp. 35–110.
MakinsonD. A tale of five cities. In: Hansson (ed.), David Makinson on classical methods for non-classical problems. New York: Springer, 2014, pp. 19–32.
13.
GebserMKaminskiRKaufmannB, et al. Answer set solving in practice. Synth Lect Artif Intell Mach Learn2012; 6: 1–238.
14.
StolpeAHannayJE. Quantifying means-end reasoning skills in simulation-based training: a logic-based approach. Simulation2022; 98: 933–957.
15.
ZeiglerBP. Hierarchical, modular discrete-event modelling in an object-oriented environment. Simulation1987; 49: 219–230.
16.
ChowACHZeiglerBP. Parallel DEVS: a parallel, hierarchical, modular modeling formalism In: Proceedings of winter simulation conference, Lake Buena Vista, FL, 11–14 December 1994, pp. 716–722. New York: IEEE.
17.
ChoSMKimTG. Real-time DEVS simulation: concurrent, time-selective execution of combined RT-DEVS model and interactive environment. In: RedmillF (ed.), Proceedings of SCSC-98, SCS Publications, Reno, NV, 19–22 July 1998, pp. 410–415.
18.
VangheluweH. The discrete event system specification (DEVS) formalism. Tech Rep2001.
19.
BaralC. Knowledge representation, reasoning and declarative problem solving. Cambridge: Cambridge University Press, 2003.
20.
PearceD. Equilibrium logic. Ann Math Artif Intell2006; 47: 3–41.
21.
GebserMKaminskiRKaufmannB, et al. Answer set solving in practice. Kentfield, CA: Morgan & Claypool Publishers, 2012.
22.
CristiáM. Formalizing the semantics of modular DEVS models with temporal logic. In: Proceeedings of 7me Conf’erence Internationale de Mod’elisation, Optimisation et Simulation des Systemes: Communication, Coop’eeration et Coordination (MOSIM 08), Paris, 31 March–2 April 2008.
23.
AriasJCarroMSalazarE, et al. Constraint answer set programming without grounding. Theor Pract Log Program2018; 18: 337–354.
MojtahedVAnderssonBKabilanV, et al. BOM++, a semantically enriched BOM. In: Proceedings of the 2008 spring simulation interoperability workshop (SIW). Orlando, FL, 14–18 April 2008.
27.
De NicolaAMelchioriMVillaniML. Creative design of emergency management scenarios driven by semantics: an application to smart cities. Inf Syst2019; 81: 21–48.
28.
SteelJIannellaRLamHP. Using ontologies for decision support in resource messaging. In: Proceedings of the 5th international conference on information systems for crisis response and management (ISCRAM), Washington, DC, 5–7 May 2008.
29.
B’enabenFHanachiCLaurasM, et al. A metamodel and its ontology to guide crisis characterization and its collaborative management. In: Proceedings of the 5th international conference on information systems for crisis response and management (ISCRAM), Washington, DC, 4–7 May 2008.
30.
SingapoguSSGuptonKSchadeU. The role of ontology in C2SIM. In: Proceedings of the 21st international command and control research and technology symposium (ICCRTS 2016), London, 6–8 September 2016.
van den BergTWHuiskampWSiegfriedR, et al. Modelling and simulation as a service: rapid deployment of interoperable and credible simulation environments—an overview of NATO MSG-136. In: Proceedings of the 2018 Winter Simulation Innovation Workshop, Orlando, FL, 21–26 January 2018.
33.
KasımBCAvdarABNacarMA, et al. Modeling and simulation as a service for joint military space operations simulation. J Def Model Simul2021; 18: 29–38.
34.
HannayJEvan den BergTGallantS, et al. Modeling and simulation as a service infrastructure capabilities for discovery, composition and execution of simulation services. J Def Model Simul2020; 1: 5–28.
35.
HannayJEvan den BergTW. The NATO MSG-136 reference architecture for M&S as a service. In: Proceedings of the NATO modelling and simulation group symposium on M&S technologies and standards for enabling alliance interoperability and pervasive M&S applications (STO-MP-MSG-149), Lisbon, 19–20 October 2017.
36.
MentréDMarchéCFilliâtreJC, et al. Discharging proof obligations from Atelier B using multiple automated provers. In: DerrickJFitzgeraldJGnesiS, et al. (eds) Abstract state machines, alloy, B, VDM, and Z. Heidelberg: Springer, 2012, pp. 238–251.
37.
AbrialJR. The B-book: assigning programs to meanings. Cambridge: Cambridge University Press, 1996.
38.
MorrisKSnookCHoangTS, et al. Formal verification and validation of run-to-completion style state charts using Event-B. Innov Syst Softw Eng2022; 18: 523–541.
39.
GaoYZhangYZhouX, et al. Overview of simulation architectures supporting live virtual constructive (LVC) integrated training. In: 2021 6th international conference on control, robotics and cybernetics (CRC), Shanghai, 9–11 October 2021, pp. 333–338. New York: IEEE.
40.
HodickyJProchazkaDProchazkaJ. Automation in experimentation with constructive simulation. In: MazalJ (ed.) Modelling and simulation for autonomous systems. Cham: Springer International Publishing, 2019, pp. 566–576.
41.
GrigoryanGEtemadidavanSCollinsAJ. Computerized agents versus human agents in finding core coalition in glove games. Simulation2022; 98: 807–821.
42.
LøvlidRABruvollSBrathenK, et al. Modeling the behavior of a hierarchy of command agents with context-based reasoning. J Def Model Simul2018; 15: 369–381.
43.
ZakariaN. Action network: a probabilistic graphical model for social simulation. Simulation2022; 98: 335–346.
AngilicaDIanniGPacenzaF. Tight integration of rule-based tools in game development. In: AlvianoMGrecoGScarcelloF (eds) AI*IA 2019—advances in artificial intelligence. Cham: Springer International Publishing, 2019, pp. 3–17.
46.
CalimeriFFuscáDGermanoS. Fostering the use of declarative formalisms for real-world applications: the embasp framework. New Gener Comput2019; 37: 29–65.
GelfondMLifschitzV. The stable model semantics for logic programming. In: KowalskiRBowenK (eds) Proceedings of international logic programming conference and symposium. Cambridge, MA: MIT Press, 1988, pp. 1070–1080.
49.
GelfondMLifschitzV. Classical negation in logic programs and disjunctive databases. New Gener Comput1991; 9: 365–385.
KowalskiR. Computational logic and human thinking: how to be artificially intelligent. Cambridge: Cambridge University Press, 2011.
52.
GiunchigliaELeeJLifschitzV, et al. Nonmonotonic causal theories. Artif Intell2004; 153: 49–104.
53.
MuellerET. Chapter 17 event calculus. In: van HarmelenFLifschitzVPorterB (eds) Handbook of knowledge representation, foundations of artificial intelligence. Vol. 3. Amsterdam: Elsevier, 2008, pp. 671–708.
54.
OberschelpA. Order sorted predicate logic. In: BläsiusKHHedtstückURollingerCR (eds) Sorts and types in artificial intelligence. Heidelberg: Springer, 1990, pp. 7–17.
55.
MuellerET. Commonsense reasoning: an event calculus based approach. Burlington, MA: Morgan Kaufmann, 2014.
56.
ReiterR. On closed world data bases. In: WebberBLNilssonNJ (eds) Readings in artificial intelligence. Burlington, MA: Morgan Kaufmann, 1981, pp. 119–140.
57.
KaufmannBLeoneNPerriS, et al. Grounding and solving in answer set programming. AI Magazine2016; 37: 25–32.
58.
GebserMSchaubTThieleS. Gringo: a new grounder for answer set programming. In: BaralCBrewkaGSchlipfJ (eds) International conference on logic programming and nonmonotonic reasoning. Heidelberg: Springer, 2007, pp. 266–271.
59.
BreienFSWassonB. Narrative categorization in digital game-based learning: engagement, motivation & learning. Br J Educ Technol2021; 52: 91–111.
60.
SubhashSCudneyEA. Gamified learning in higher education: a systematic review of the literature. Comput Hum Behav2018; 87: 192–206.
61.
CamposNNogalMCalizC, et al. Simulation-based education involving online and on-campus models in different European universities. Int J Educ Technol High Educ2020; 17: 8.
