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Abdul Razak

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3 papers

2 author rows

AAAI Conference 2017 Conference Paper

Multi-Objective Influence Diagrams with Possibly Optimal Policies

Radu Marinescu
Abdul Razak
Nic Wilson

The formalism of multi-objective inﬂuence diagrams has recently been developed for modeling and solving sequential decision problems under uncertainty and multiple objectives. Since utility values representing the decision maker’s preferences are only partially ordered (e. g. , by the Pareto order) we no longer have a unique maximal value of expected utility, but a set of them. Computing the set of maximal values of expected utility and the corresponding policies can be computationally very challenging. In this paper, we consider alternative notions of optimality, one of the most important one being the notion of possibly optimal, namely optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop a variable elimination algorithm for computing the set of possibly optimal expected utility values, prove formally its correctness, and compare variants of the algorithm experimentally.

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IJCAI Conference 2015 Conference Paper

Computing Possibly Optimal Solutions for Multi-Objective Constraint Optimisation with Tradeoffs

Nic Wilson
Abdul Razak
Radu Marinescu

Computing the set of optimal solutions for a multiobjective constraint optimisation problem can be computationally very challenging. Also, when solutions are only partially ordered, there can be a number of different natural notions of optimality, one of the most important being the notion of Possibly Optimal, i. e. , optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop an AND/OR Branch-and-Bound algorithm for computing the set of Possibly Optimal solutions, and compare variants of the algorithm experimentally.

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UAI Conference 2012 Conference Paper

Multi-objective Influence Diagrams

Radu Marinescu 0002
Abdul Razak
Nic Wilson

We describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp , and are typically only partially ordered. These can still be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ǫ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user tradeoffs, which also greatly improves the efficiency.

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