Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject...

Decision-making tasks sometimes involve soft objectives. They are soft in the sense that they contain uncertainty, imprecision or vagueness. For example, decisions on built environment aim to maximize comfort or other experiential qualities. Pareto-optimal solutions to such problems can be found using multi-objective evolutionary search together with other soft computing methods. Beyond optimal...

In order to make a decision in the face of multiple objectives it is necessary to know the relative importance of the different objectives. Yet, it is often very difficult to specify a set of precise weights before possible alternatives solutions are known. In this paper, we present an interactive, iterative method for arriving at an acceptable solution. The decision maker gradually discerns wh...

19 This study introduces a new open source software framework to support bottom-up environmental 20 systems planning under deep uncertainty with a focus on many-objective robust decision making 21 (MORDM), called OpenMORDM. OpenMORDM contains two complementary components: (1) a 22 software application programming interface (API) for connecting planning models to computational 23 exploration too...

In a bilevel decision problem, the leader at the upper level attempts to optimize his/her objective, the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. A bilevel decision problem may involve fuzzy demands which appear either in the objective functions or constraints of the leader or the follower or both. Furthermore, t...

My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need to make a single collective decision: convex multiobjective variable elimination (CMOVE) and varia...

Concave properties play a dominate role in solving both classic and fuzzy optimization problems. However, since fuzzy problems are generally represented by sets, not crisp numbers, various aggregation schemes are needed to manipulate and to combine the different elements in a fuzzy optimization problem. Based on these different aggregations, various concavity properties can be formulated and ex...

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user’s intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this g...

Investment in landscapes to achieve outcomes that have multiple environmental benefits has become a major priority in many countries. This gives rise to opportunities for mathematical programming methods to provide solutions on where investments could be made on the landscape, to maximise multiple environmental benefits. The problem was formulated as a multi-objective integer programming model,...