67 citations · 262 across the 30 of their papers we have counts for
11 papers · 1 filter
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…
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Emanuele Albini, Shubham Sharma, Saumitra Mishra +2
Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterf…
Explaining Preference-driven Schedules: the EXPRES Framework
Alberto Pozanco, Francesca Mosca, Parisa Zehtabi +2
Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get.…
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…
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…
Explainable Planning
Maria Fox, Derek Long, Daniele Magazzeni
As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated…
PDDL+ Planning via Constraint Answer Set Programming
Marcello Balduccini, Daniele Magazzeni, Marco Maratea
PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Co…
Plan-based Policies for Efficient Multiple Battery Load Management
Maria Fox, Derek Long, Daniele Magazzeni
Efficient use of multiple batteries is a practical problem with wide and growing application. The problem can be cast as a planning problem under uncertainty. We describe the appro…
Resource-Optimal Planning For An Autonomous Planetary Vehicle
Giuseppe Della Penna, Benedetto Intrigila, Daniele Magazzeni +1
Autonomous planetary vehicles, also known as rovers, are small autonomous vehicles equipped with a variety of sensors used to perform exploration and experiments on a planet's surf…