Interval Markov decision processes (IMDPs) generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. In this paper, we consider the problem of multi-objective robust strategy synthesis for interval MDP...

We study and provide efficient algorithms for multi-objective model checking problems for Markov Decision Processes (MDPs). Given an MDP, M , and given multiple linear-time (ω-regular or LTL) properties φi, and probabilities ri ∈ [0, 1], i = 1, . . . , k, we ask whether there exists a strategy σ for the controller such that, for all i, the probability that a trajectory of M controlled by σ sati...

Clustering is an important problem in knowledge discovery and decision making. In this study, a multi-objective genetic algorithm (MOGA) is used to search for well separated clusters and each evolved solution is evaluated by both data-driven and human-driven metrics developed for this study. The proposed system in this paper also allows the decision maker to navigate non-dominated solutions and...

We introduce and study a family of models for multi-expert multi-objective/criteria decision making. These models use a concept of weight robustness to generate a risk averse decision. In particular, the multi-expert multi-criteria robust weighted sum approach (McRow) introduced in this paper identifies a (robust) Pareto optimum decision that minimizes the worst case weighted sum of objectives ...

In decision-theoretic planning problems, such as (partially observable) Markov decision problems [Wiering and Van Otterlo, 2012] or coordination graphs [Guestrin et al., 2002], agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives, e.g., maximizing the economic benefits of timber harvesting w...

We consider learning tasks where multiple target variables need to be predicted. Two approaches have been used in this setting: (a) build a separate single-target model for each target variable, and (b) build a multi-target model that predicts all targets simultaneously; the latter may exploit potential dependencies among the targets. For a given target, either (a) or (b) can yield the most acc...

Among the several tasks that evolutionary algorithms have successfully employed, the induction of classification rules and decision trees has been shown to be a relevant approach for several application domains. Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, conventionally used decision trees induction algorithm...