| Abstract |
This paper proposes a military scenario based on logistical resupply from a military base to coalition forces located in a nearby urban area or city. We describe the scenario and accompanying policy such that the context of the resupply missions changes over time. The set of policies and related changes over time have been manually defined using a set of human created rules to replicate how policies are created by humans. We show how inductive learning of answer set programs can successfully learn ASG generative policy models that capture the human-driven rules from just example traces and decisions made at different time points and with respect to different contextual situations that can arise during the resupply mission. These results demonstrate the utility of ASG generative policy as a method for modelling human-driven policy rules. |
| Authors |
- Graham White (IBM UK)
- John Ingham (Dstl)
- Mark Law (Imperial)
- Alessandra Russo (Imperial)
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| Date |
Sep-2019 |
| Venue |
Annual Fall Meeting of the DAIS ITA, 2019 |
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| Variants |
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