@article {43184,
	title = {A logic-based event controller for means-end reasoning in simulation environments},
	journal = {SIMULATION},
	volume = {61},
	year = {2023},
	month = {03/2023},
	publisher = {SAGE journals},
	abstract = {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.},
	issn = {0037-5497},
	doi = {10.1177/00375497231157384},
	url = {http://journals.sagepub.com/doi/10.1177/00375497231157384http://journals.sagepub.com/doi/pdf/10.1177/00375497231157384http://journals.sagepub.com/doi/pdf/10.1177/00375497231157384http://journals.sagepub.com/doi/full-xml/10.1177/00375497231157384},
	author = {Stolpe, Audun and Rummelhoff, Ivar and Jo Erskine {Hannay}}
}
@inproceedings {Stolpe-Hannay-2021,
	title = {On the Adaptive Delegation and Sequencing of Actions},
	journal = {Proceedings of the 18th Int{\textquoteright}l Conf. Information on Information Systems for Crisis Response and Management (ISCRAM{\textendash}-AI and Intelligent Systems for Crises and Risks},
	year = {2021},
	month = {2021},
	publisher = {International Association for Information Systems for Crisis Response and Management},
	abstract = {Information systems support to crisis response and management relies on presenting actionable information in a manner that supports cognitive processes. We outline how AI Planning can be used viably to support the delegation and sequencing of tasks, using standard operating procedures as initial specifications of plans. When expressed in the AI planning language Answer set Programming (ASP), machine reasoning can be used in a pre-incident review to display the cooperative structure inherent in a plan. The purpose of this is to uncover weaknesses and to optimize the plan. Further, adaptive planning can be supported in during-incident reviews by updating the current status and recomputing the consequences. At this point, initial goals may no longer be viable and the explicit suggestion of prior sub-optimal goals now worth pursuing can be a game-changer under stress.},
	author = {Stolpe, Audun and Jo Erskine {Hannay}},
	editor = {Amanda Lee {Hughes} and McNeill, Fiona and Zobel, Christopher}
}
@inbook {10.1007/978-3-030-90963-5_32,
	title = {Toward AI-Based Scenario Management for Cyber Range Training},
	booktitle = {HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence},
	number = {LNCS 13095},
	year = {2021},
	month = {11/2021},
	pages = {423{\textendash}436},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {There is an immediate need for a greater number of highly skilled cybersecurity personnel to meet intensified cyber attacks. We propose a cyber range exercise management architecture that employs machine reasoning to structure the design, execution and analysis of cyber range training scenarios. The scenarios are then used in simulation-based training in an emulated IT infrastructure environment. The machine reasoning is obtained by combining four AI methods: attack-defence trees, formal argumentation theory, answer set programming and multiagent systems. We argue that this type of advanced functionality that supports exercise managers in their design and analysis of scenarios is strictly necessary to improve current exercise management systems and build the required cybersecurity expertise.},
	isbn = {978-3-030-90963-5},
	author = {Jo Erskine {Hannay} and Stolpe, Audun and Muhammad Mudassar {Yamin}},
	editor = {Stephanidis, Constantine and Kurosu, Masaaki and Jessie Y. C. {Chen} and Fragomeni, Gino and Streitz, Norbert and Konomi, Shin{\textquoteright}ichi and Degen, Helmut and Ntoa, Stavroula}
}
@inproceedings {RouwendalvanSchijndel-Stolpe-Hannay-2021,
	title = {Toward an AI-based external scenario event controller for crisis response simulations},
	journal = {Proceedings of the 18th Int{\textquoteright}l Conf. Information on Information Systems for Crisis Response and Management (ISCRAM{\textendash}-AI and Intelligent Systems for Crises and Risks},
	year = {2021},
	publisher = {International Association for Information Systems for Crisis Response and Management},
	abstract = {There is a need for tool support for structured planning, execution and analysis of simulation-based training for crisis response and management. As a central component of an architecture for such tool support, we outline the design of an AI-based scenario event controller. The event controller is a component that uses machine reasoning to compute the next state in a scenario, given the actions performed in the corresponding simulation (execution of the scenario). Scenarios are specified in Answer Set Programming (ASP), which is a logic programming language we use for automated planning of training scenarios. A plan encoding in ASP adds expressivity in scenario specification and enables machine reasoning. For exercise managers this gives AI-based tool support for before-action and during-action reviews to optimize learning. In line with Modelling and Simulation as as Service, our approach externalizes event control from any particular simulation platform. The scenario, and its unfolding in terms of events, is externalized as a service. This increases interoperability and enables scenarios to be designed and modified readily and rapidly to adapt to new training requirements.},
	author = {Dashley Kevin {Rouwendal} and Stolpe, Audun and Jo Erskine {Hannay}},
	editor = {Adrot, Anouck and Grace, Rob and Moore, Kathleen and Zobel, Christopher}
}
@inbook {10.1007/978-3-030-50732-9_60,
	title = {Using Block-Based Programming and Sunburst Branching to Plan and Generate Crisis Training Simulations},
	booktitle = { Communications in Computer and Information Science },
	volume = {1226},
	year = {2020},
	pages = {463{\textendash}471},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {HCI International 2020 - Posters},
	address = {Cham},
	abstract = {Simulation-based exercises for crisis response are difficult to plan. We suggest an intuitive planning interface where exercise managers can use block-based programming to create machine-readable training vignettes. An answer set programming module interprets these vignettes and generates all possible actions and causal relations, which are then visualised for the exercise manager in a sunburst diagram. This allows exercise managers to create training vignettes using visual techniques and to see the possible results and causal relations of the trainees{\textquoteright} actions. This facilitates using exercise results to adjust further vignette design.},
	isbn = {978-3-030-50732-9},
	author = {Dashley K. Rouwend {van Schijndel} and Stolpe, Audun and Jo Erskine {Hannay}},
	editor = {Stephanidis, Constantine and Antona, Margherita}
}
