Principles of artificial intelligence
Principles of artificial intelligence
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An axiomatic treatment of three qualitative decision criteria
Journal of the ACM (JACM)
Multi-criteria Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A polynomial algorithm for decentralized Markov decision processes with temporal constraints
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Decision Analysis
Towards a formal framework for multi-objective multiagent planning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Algebraic Markov decision processes
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Topological order planner for POMDPs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decision-theoretic robot guidance for active cooperative perception
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An investigation into mathematical programming for finite horizon decentralized POMDPs
Journal of Artificial Intelligence Research
UCP-networks: a directed graphical representation of conditional utilities
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Human-robot interaction in rescue robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Path planning of mobile robot with neuro-genetic-fuzzy technique in static environment
International Journal of Hybrid Intelligent Systems
Hi-index | 0.00 |
The problem of path planning in stochastic environments where the shortest path is not always the best one is a challenging issue required in many real-world applications such as autonomous vehicles, robotics, logistics, etc. … In this paper, we consider the problem of path planning in stochastic environments where the length of the path is not the unique criterion to consider. We formalize this problem as a multi-objective decision-theoretic path planning and we transform this latter into 2VMDP Vector-Valued Markov Decision Process. We show, then, how we can compute a policy balancing between different considered criteria. We describe different techniques that allow us to derive an optimal policy where it is hard to express the expected utilities, rewards and values with a unique numerical measure. Firstly, we examine different existing approaches based on preferences and we define notions of optimality with preferred solutions and secondly we present approaches based on egalitarian social welfare techniques. Finally, some experimental results have been developed to show the feasibility of the approach and the benefit of this approach on the single-objective techniques.