no code implementations • 2 Nov 2020 • Abhinav Sharma, Advait Deshpande, Yanming Wang, Xinyi Xu, Prashan Madumal, Anbin Hou
We propose a novel non-randomized anytime orienteering algorithm for finding k-optimal goals that maximize reward on a specialized graph with budget constraints.
1 code implementation • 27 Jun 2020 • Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein
Based on the requirements of fidelity (approximate models to target models) and interpretability (being meaningful to people), we design measurements and evaluate a range of matrix factorization methods with our framework.
no code implementations • 28 Jan 2020 • Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere
In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions.
2 code implementations • 27 May 2019 • Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere
In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents.
no code implementations • 5 Mar 2019 • Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 21 Jun 2018 • Prashan Madumal, Tim Miller, Frank Vetere, Liz Sonenberg
We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)