16 citations · 28 across the 10 of their papers we have counts for
12 papers
Beyond Manual Planning: Seating Allocation for Large Organizations
Anton Ipsen, Michael Cashmore, Kirsty Fielding +4
We introduce the Hierarchical Seating Allocation Problem (HSAP) which addresses the optimal assignment of hierarchically structured organizational teams to physical seating arrange…
The Subset Sum Matching Problem
Yufei Wu, Manuel R. Torres, Parisa Zehtabi +4
This paper presents a new combinatorial optimisation task, the Subset Sum Matching Problem (SSMP), which is an abstraction of common financial applications such as trades reconcili…
Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels
Danial Dervovic, Michael Cashmore
Missing data in supervised learning is well-studied, but the specific issue of missing labels during model evaluation has been overlooked. Ignoring samples with missing values, a c…
Temporal Fairness in Decision Making Problems
Manuel R. Torres, Parisa Zehtabi, Michael Cashmore +2
In this work we consider a new interpretation of fairness in decision making problems. Building upon existing fairness formulations, we focus on how to reason over fairness from a…
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti +3
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance…
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Saeid Amiri, Parisa Zehtabi, Danial Dervovic +1
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we exam…
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Kyle Mana, Fernando Acero, Stephen Mak +4
Discrete optimization belongs to the set of -hard problems, spanning fields such as mixed-integer programming and combinatorial optimization. A current standard appro…
Contrastive Explanations of Plans Through Model Restrictions
Benjamin Krarup, Senka Krivic, Daniele Magazzeni +3
In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user's expectation. We frame Explainable AI Planning in the context…
Robust Plan Execution with Unexpected Observations
Oscar Lima, Michael Cashmore, Daniele Magazzeni +2
In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which c…
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans
Michael Cashmore, Alessandro Cimatti, Daniele Magazzeni +2
One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the…
Towards Explainable AI Planning as a Service
Michael Cashmore, Anna Collins, Benjamin Krarup +3
Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a servi…
Towards Providing Explanations for AI Planner Decisions
Rita Borgo, Michael Cashmore, Daniele Magazzeni
In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifica…