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Jonathan Ferrer-Mestres

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3 papers

2 author rows

ECAI Conference 2023 Conference Paper

Synthesis of Procedural Models for Deterministic Transition Systems

Javier Segovia-Aguas
Jonathan Ferrer-Mestres
Sergio Jiménez Celorrio

This paper introduces a general approach for synthesizing procedural models of the state-transitions of a given discrete system. The approach is general in that it accepts different target languages for modeling the state-transitions of a discrete system; different model acquisition tasks with different target languages, such as the synthesis of STRIPS action models, or the update rule of a cellular automaton, fit as particular instances of our general approach. We follow an inductive approach to synthesis meaning that a set of examples of state-transitions, represented as (pre-state, action, post-state) tuples, are given as input. The goal is to synthesize a structured program that, when executed on a given pre-state, outputs its associated post-state. Our synthesis method implements a combinatorial search in the space of well-structured terminating programs that can be built using a Random-Access Machine (RAM), with a minimalist instruction set, and a finite amount of memory. The combinatorial search is guided with functions that asses the complexity of the candidate programs, as well as their fitness to the given input set of examples.

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AAAI Conference 2021 Conference Paper

K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management

Jonathan Ferrer-Mestres
Thomas G. Dietterich
Olivier Buffet
Iadine Chades

In biodiversity conservation, adaptive management (AM) is the principal tool for decision making under uncertainty. AM problems are planning problems that can be modelled using Mixed Observability MDPs (MOMDPs). MOMDPs tackle decision problems where state variables are completely or partially observable. Unfortunately, MOMDP solutions (policy graphs) are too complex to be interpreted by human decision-makers. Here, we provide algorithms to solve K- N-MOMDPs, where K represents the maximum number of fully observable states and N represents the maximum number of α-vectors. Our algorithms calculate compact and more interpretable policy graphs from existing MOMDP models and solutions. We apply these algorithms to two computational sustainability applications: optimal release of biocontrol agents to prevent dengue epidemics and conservation of the threatened bird species Gouldian finch. The methods dramatically reduce the number of states and α-vectors in MOMDP problems without significantly reducing their quality. The resulting policies have small policy graphs (4- 6 nodes) that can be easily interpreted by human decisionmakers.

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ICAPS Conference 2020 Conference Paper

Solving K-MDPs

Jonathan Ferrer-Mestres
Thomas G. Dietterich
Olivier Buffet
Iadine Chades

Markov Decision Processes (MDPs) are employed to model sequential decision-making problems under uncertainty. Traditionally, algorithms to solve MDPs have focused on solving large state or action spaces. With increasing applications of MDPs to human-operated domains such as conservation of biodiversity and health, developing easy-to-interpret solutions is of paramount importance to increase uptake of MDP policies. Here, we define the problem of solving K-MDPs, i. e. , given an original MDP and a constraint on the number of states (K), generate a reduced state space MDP that minimizes the difference between the original optimal MDP value function and the reduced optimal K-MDP value function. Building on existing non-transitive and transitive approximate state abstraction functions, we propose a family of three algorithms based on binary search with sub-optimality bounded polynomially in a precision parameter: ϕQ*εK-MDP-ILP, ϕQ*dK-MDP and ϕa*dK-MDP. We compare these algorithms to a greedy algorithm (ϕQ*ε Greedy K-MDP) and clustering approach (k-means++ K-MDP). On randomly generated MDPs and two computational sustainability MDPs, ϕa*dK-MDP outperformed all algorithms when it could find a feasible solution. While numerous state abstraction problems have been proposed in the literature, this is the first time that the general problem of solving K-MDPs is suggested. We hope that our work will generate future research aiming at increasing the interpretability of MDP policies in human-operated domains.

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