Knowledge-based reactive planning and replanning

Djemai, Ramzi, Vassilev, Vassil, Ouazzane, Karim and Dey, Maitreyee (2024) Knowledge-based reactive planning and replanning. In: IEEE CAI 2024 - Conference on Artificial Intelligence, 25-27 June 2024, Marina Bay Sands, Singapore. (In Press)


When a disaster strikes; man-made or natural–evacuation plans are put under immediate constraints, including topological, temporal, and spontaneously occurring events such as fire, smoke and obstacles introducing bottlenecks and impeding ingress and egress. Planning for uncertainties arising from indoor evacuations can be complex as there’s a fine balance to strike between a too-detailed plan and one that’s too vague. Such constraints apply to office and residential buildings, airports, mining sites, stadiums, ships, etc. Although some indoor spatial models have been developed, many are complex, and their applicability is non-universal. This paper proposes an innovative approach that harnesses the power of the Semantic Web Rule Language (SWRL) based on Web Ontology Language (OWL) to enhance existing evacuation planning methods through data-rich modelling. The OWL ontology serves as a formal representation of real-world concepts, their relationships, and properties. To demonstrate its application, the ontology is implemented in a case study involving London Metropolitan University’s Tower Building, and its design is elucidated in this paper.

VV-40-Knowledge_based_Reactive_Planning_and_Re_planning_A_Case_Study_Approach.pdf - Accepted Version
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