Smart portable applications increasingly rely on edge computing due to privacy andlatencyconcerns.

Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure.

Many applications require computing likelihoods and marginal probabilities over a distribution defined by a graphical model, tasks which are intractable in general [24].

The proposed method offers three unique advantages.

Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes.

The solution in [5,4]requires aone-pass forward computation of marginal probabilities.

Thepartially observableMarkovdecision process (POMDP) provides ageneral framework for modeling an agent's decision process with state uncertainty, and online planning plays a pivotal role in solving it. A belief is a distribution of states representing state uncertainty. Methods forlarge-scale POMDP problems rely on the same idea of sampling both states and observations.

Weconsider Boolean satisfiability problems fromdifferentclasses and learn specialized heuristics for each class.

Wefirstprovethat"delayed"Exp3achievesthe O p (KT +D)lnK regret bound conjectured by Cesa-Bianchi et al. [2019] in the case of variable, but bounded delays. Here,K is the number of actions andD isthe total delay overT rounds.

Conditioning on the current game context, the generation model predicts the distribution of the currently acting player.