Ph.D
Group : Learning and Optimization

Multi-Objective Sequential Decision Making

Starts on 23/09/2010
Advisor : SEBAG, Michèle
[SCHOENAUER Marc]

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 11/07/2014, committee :

Research activities :

Abstract :
This thesis is concerned with multi-objective sequential decision making (MOSDM).
The motivation is twofold. On the one hand, many decision problems in the domains of e.g., robotics, scheduling or games, involve the optimization of sequences of decisions. On the other hand, many real-world applications are most naturally formulated in terms
of multi-objective optimization (MOO).

The proposed approach extends the well-known Monte-Carlo tree search (MCTS) framework to the MOO setting, with the goal of discovering several optimal sequences of decisions through growing a single search tree. The main challenge is to propose a new reward, able to guide the exploration of the tree although the MOO setting does not enforce a total order among solutions.

The main contribution of the thesis is to propose and experimentally study two such rewards, inspired from the MOO literature and assessing a solution with respect to the archive of previous solutions (Pareto archive): the hypervolume indicator and the Pareto dominance reward.

The study shows the complementarity of these two criteria. The hypervolume indicator suers from its known computational complexity; however the proposed extension thereof provides ne-grained information abut the quality of solutions with respect to the current archive. Quite the contrary, the Pareto-dominance reward is linear but it provides increasingly rare information.

Proofs of principle of the approach are given on articial problems and challenges, and confirm the merits of the approach. In particular, MOMCTS is able to discover policies lying in non-convex regions of the Pareto front, contrasting with the state of the art: existing Multi-Objective Reinforcement Learning algorithms are based on linear scalarization and thus fail to sample such non-convex regions.

Finally MOMCTS honorably competes with the state of the art on the 2013 MOPTSP competition.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.