In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: Developing effective biologically plausible learning rules for deep neural networks is important for advancing connections between deep learning and neuroscience. To date, local synaptic learning rules like those employed by the brain have failed to match the performance of backpropagation in deep networks. In this work, we employ meta-learning to discover networks that learn using feedback connections and local, biologically motivated learning rules. Importantly, the feedback connections are not tied to the feedforward weights, avoiding any biologically implausible weight transport. It can be shown mathematically that this approach has sufficient expressivity to approximate any online learning algorithm. Our experiments show that the meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures. Moreover, we demonstrate empirically that this model outperforms a state-of-the-art gradient-based meta-learning algorithm for continual learning on regression and classification benchmarks. This approach represents a step toward biologically plausible learning mechanisms that can not only match gradient descent-based learning, but also overcome its limitations.
[A]: Networks that learn with feedback connections and local plasticity rules can be optimized for using meta learning.

[Q]: Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.
[A]: We present a Bayesian inference model to infer contrastive explanations (as LTL specifications) describing how two sets of plan traces differ.

[Q]: Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to minimize the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.
[A]:
"Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently using deep reinforcement learning.