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Many real-world optimization problems involve balancing multiple objectives. When there is no solution that is best with respect to all objectives, it is often desirable to compute the Pareto front. This paper proposes queued Pareto local search (QPLS), which improves on existing Pareto local search (PLS) methods by maintaining a queue of improvements… (More)
This paper gives a description of an approach to analyze the sensor information of the surroundings to select places where the foot of a humanoid can be placed. This will allow apply such robot in a rescue scenario, as foreseen in the DARPA Robotics Challenge, where a robot is forced to traverse difficult terrain.
Standard single-objective methods such as value iteration are not applicable to multi-objective Markov decision processes (MOMDPs) because they depend on a maximization, which is not defined if the rewards are multi-dimensional. As a result, special multi-objective algorithms are needed to find a set of policies that contains all optimal trade-offs between… (More)