The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours.

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We develop refined non-asymptotic lower bounds, which show that optimality mandates very different sampling behavior for a low vs high true minimum. We show that Thompson Sampling and the intuitive Lower Confidence Bounds policy each nail only one of these cases. We develop a novel approach that we call Murphy Sampling. Even though it entertains exclusively low true minima, we prove that MS is optimal for both possibilities. We then design advanced self-normalized deviation inequalities, fueling more aggressive stopping rules. We complement our theoretical guarantees by experiments showing that MS works best in practice.

Program synthesis is the process of automatically translating a specification into computer code. Traditional synthesis settings require a formal, precise specification. Motivated by computer education applications where a student learns to code simple turtle-style drawing programs, we study a novel synthesis setting where only a noisy user-intention drawing is specified. This allows students to sketch their intended output, optionally together with their own incomplete program, to automatically produce a completed program. We formulate this synthesis problem as search in the space of programs, with the score of a state being the Hausdorff distance between the program output and the user drawing. We compare several search algorithms on a corpus consisting of real user drawings and the corresponding programs, and demonstrate that our algorithms can synthesize programs optimally satisfying the specification.

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving $10^{13}$ states without using a separate task planner.

An almost-perfect chess playing agent has been a long standing challenge in the field of Artificial Intelligence. Some of the recent advances demonstrate we are approaching that goal. In this project, we provide methods for faster training of self-play style algorithms, mathematical details of the algorithm used, various potential future directions, and discuss most of the relevant work in the area of computer chess. Deep Pepper uses embedded knowledge to accelerate the training of the chess engine over a "tabula rasa" system such as Alpha Zero. We also release our code to promote further research.

We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up beam search for sequence models. We utilize the winner-take-all (WTA) hash, which is based on relative ranking order of hidden dimensions and thus resilient to perturbations in numerical values. Our algorithm is designed by fully considering the underling architecture of CUDA-enabled GPUs (Algorithm/Architecture Co-design): 1) A parallel Cuckoo hash table is applied for LSH code lookup (guaranteed O(1) lookup time); 2) Candidate lists are shared across beams to maximize the parallelism; 3) Top frequent words are merged into candidate lists to improve performance. Experiments on 4 large-scale neural machine translation models demonstrate that our algorithm can achieve up to 4x speedup on softmax module, and 2x overall speedup without hurting BLEU on GPU.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

The complexity classes PPA and PPAD were introduced in a seminal paper of Papadimitriou [47] in 1994, in an attempt to classify several natural problems in the class TFNP [45]. TFNP is the class of total search problems in NP for which a solution exists for every instance and the solution can be efficiently verified. Various important problems were subsequently proven to be complete for the class PPAD, such as the complexity of many versions of Nash equilibrium [16, 11, 21, 46, 50, 12] and market equilibrium computation [15, 9, 57, 13, 52]. For details on this, and the significance of PPAcompleteness, see the related discussion in [23], but note the following basic points. As evidence of computational hardness, PPA-completeness is stronger than PPAD-completeness: PPAD PPA. In terms of expressive power, the distinction is that PPA-complete problems can embed a search for a guaranteed fixpoint in a non-oriented topological space, but PPAD problems restrict us to an oriented one. Complete problems for the class PPA seemed to be much more elusive than PPAD-complete ones, especially when one is interested in "natural" problems, where "natural" here has the very specific meaning of problems that do not explicitly contain a circuit in their definition.

Visual storytelling includes two important parts: coherence between the story and images as well as the story structure. For image to text neural network models, similar images in the sequence would provide close information for story generator to obtain almost identical sentence. However, repeatedly narrating same objects or events will undermine a good story structure. In this paper, we proposed an inter-sentence diverse beam search to generate a more expressive story. Comparing to some recent models of visual storytelling task, which generate story without considering the generated sentence of the previous picture, our proposed method can avoid generating identical sentence even given a sequence of similar pictures.

Construct, Merge, Solve & Adapt (CMSA) [6] is a hybrid metaheuristic that can be applied to any combinatorial optimization problem for which is known a way of generating feasible solutions, and whose subproblems can be solved to optimality by a black-box solver. Moreover, note that CMSA is thought for those problem instances for which the application of 1 the standalone black-box solver is not feasible due to the problem instance size and/or difficulty. The main idea of CMSA is to generate reduced subinstances of the original problem instances, based on feasible solutions that are constructed at each iteration, and to solve these reduced instances by means of the black-box solver. Obviously, the parameters of CMSA have to be adjusted in order for the size of the reduced sub-instances to be such that the black-box solver can solve them efficiently. CMSA has been applied to several NPhard combinatorial optimization problems, including minimum common string partition [6, 4], the repetition-free longest common subsequence problem [5], and the multidimensional knapsack problem [15]. A possible disadvantage of CMSA is the fact that a problem-specific way of probabilistically generating solutions is used in the above-mentioned applications. Therefore, the goal of this paper is to design a CMSA variant that can be easily applied to different combinatorial optimization problems. One way of achieving this goal is the development of a solver for a quite general problem.