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.

Ex Input:
Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm’s best performance.

Ex Output:
Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments


Ex Input:
Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht−1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit’s effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.

Ex Output:
Training RNNs as Fast as CNNs


Ex Input:
Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashion. This position paper builds on the idea of encoding knowledge as temporally extended predictions through the use of general value functions. Prior work has focused on learning predictions about externally derived signals about a task or environment (e.g. battery level, joint position). Here we advocate that the agent should also predict internally generated signals regarding its own learning process—for example, an agent’s confidence in its learned predictions. Finally, we suggest how such information would be beneficial in creating an introspective agent that is able to learn to make good decisions in a complex, changing world. Predictive Knowledge. The ability to autonomously construct knowledge directly from experience produced by an agent interacting with the world is a key requirement for general intelligence. One particularly promising form of knowledge that is grounded in experience is predictive knowledge—here defined as a collection of multi-step predictions about observable outcomes that are contingent on different ways of behaving. Much like scientific knowledge, predictive knowledge can be maintained and updated by making a prediction, executing a procedure, and observing the outcome and updating the prediction—a process completely independent of human intervention. Experience-grounded predictions are a powerful resource to guide decision making in environments which are too complex or dynamic to be exhaustively anticipated by an engineer [1,2]. A value function from the field of reinforcement learning is one way of representing predictive knowledge. Value functions are a learned or computed mapping from state to the long-term expectation of future reward. Sutton et al. recently introduced a generalization of value functions that makes it possible to specify general predictive questions [1]. These general value functions (GVFs), specify a prediction target as the expected discounted sum of future signals of interest (cumulants) observed while the agent selects actions according to some decision making policy. Temporal discounting is also generalized in GVFs from the conventional exponential weighting of future cumulants to an arbitrary, stateconditional weighting of future cumulants. This enables GVFs to specify a rich ar X iv :1 60 6. 05 59 3v 1 [ cs .A I] 1 7 Ju n 20 16

Ex Output:

Introspective Agents: Confidence Measures for General Value Functions