Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of u...
Bush fires cause major damage each year in many areas of the world and the earlier that they can be detected the easier it is to minimize this damage. This paper describes a coll...
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decisi...
Distributed sensor networks are highly prone to accidental errors and malicious activities, owing to their limited resources and tight interaction with the environment. Yet only a...
Claudio Basile, Meeta Gupta, Zbigniew Kalbarczyk, ...