Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) sett…
"The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all Al problems can easily be formula…
This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different…
"The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all Al problems can easily be formula…
"The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all Al problems can easily be formula…
In the observational study of social systems, the major conceptual innovation of the last century was General Systems Theory. Yet the General Systems Theory conceptions of interacting social systems were doomed to remain at the prescientific level of metaphor until a set of stat…
In the observational study of social systems, the major conceptual innovation of the last century was General Systems Theory. Yet the General Systems Theory conceptions of interacting social systems were doomed to remain at the prescientific level of metaphor until a set of stat…