In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use of any Bayesian reinforcement learning technique in this case. It can be seen as an extension of simulation methods to both planning and inference. We experimentally demonstrate the potential of this approach in a comparison with LSPI. Finally, we introduce a theorem showing that ABC is sound.

A: ABC Reinforcement Learning
****
Q: We present a new system S for handling uncertainty in a quantified modal logic (first-order modal logic). The system is based on both probability theory and proof theory. The system is derived from Chisholm’s epistemology. We concretize Chisholm’s system by grounding his undefined and primitive (i.e. foundational) concept of reasonableness in probability and proof theory. S can be useful in systems that have to interact with humans and provide justifications for their uncertainty. As a demonstration of the system, we apply the system to provide a solution to the lottery paradox. Another advantage of the system is that it can be used to provide uncertainty values for counterfactual statements. Counterfactuals are statements that an agent knows for sure are false. Among other cases, counterfactuals are useful when systems have to explain their actions to users (If I had not done α, then φ would have happened). Uncertainties for counterfactuals fall out naturally from our system. Efficient reasoning in just simple first-order logic is a hard problem. Resolution-based first-order reasoning systems have made significant progress over the last several decades in building systems that have solved non-trivial tasks (even unsolved conjectures in mathematics). We present a sketch of a novel algorithm for reasoning that extends firstorder resolution. Finally, while there have been many systems of uncertainty for propositional logics, first-order logics and propositional modal logics, there has been very little work in building systems of uncertainty for first-order modal logics. The work described below is in progress; and once finished will address this lack.

A: Strength Factors: An Uncertainty System for a Quantified Modal Logic
****
Q: Parameterized algorithms are a way to solve hard problems more efficiently, given that a specific parameter of the input is small. In this paper, we apply this idea to the field of answer set programming (ASP). To this end, we propose two kinds of graph representations of programs to exploit their treewidth as a parameter. Treewidth roughly measures to which extent the internal structure of a program resembles a tree. Our main contribution is the design of parameterized dynamic programming algorithms, which run in linear time if the treewidth and weights of the given program are bounded. Compared to previous work, our algorithms handle the full syntax of ASP. Finally, we report on an empirical evaluation that shows good runtime behaviour for benchmark instances of low treewidth, especially for counting answer sets.

A:
Answer Set Solving with Bounded Treewidth Revisited∗
****