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 develop a new semantics for defeasible infer­ ence based on extended probability measures al­ lowed to take infinitesimal values, on the inter­ pretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy-maximization.

A: Defaults and lnfinitesimals Defeasible Inference by Nonarchimedean Entropy Maximization
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Q: As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an L∗ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.

A: Supervisor Synthesis of POMDP based on Automata Learning
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Q: Solving symmetric Bayesian decision prob­ lems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improv­ ing the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evalua­ tion a principle recently shown to improve the efficiency of inference in Bayesian net­ works. The basic idea is to maintain de­ compositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evalu­ ation based method is compared with the HUGIN and valuation-based systems architec­ tures for solving symmetric Bayesian decision problems.

A:
Lazy Evaluation of Symmetric Bayesian Decision Problems
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