43,809 research outputs found
Asymmetric Action Abstractions for Multi-Unit Control in Adversarial Real-Time Games
Action abstractions restrict the number of legal actions available during
search in multi-unit real-time adversarial games, thus allowing algorithms to
focus their search on a set of promising actions. Optimal strategies derived
from un-abstracted spaces are guaranteed to be no worse than optimal strategies
derived from action-abstracted spaces. In practice, however, due to real-time
constraints and the state space size, one is only able to derive good
strategies in un-abstracted spaces in small-scale games. In this paper we
introduce search algorithms that use an action abstraction scheme we call
asymmetric abstraction. Asymmetric abstractions retain the un-abstracted
spaces' theoretical advantage over regularly abstracted spaces while still
allowing the search algorithms to derive effective strategies, even in
large-scale games. Empirical results on combat scenarios that arise in a
real-time strategy game show that our search algorithms are able to
substantially outperform state-of-the-art approaches.Comment: AAAI'1
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the
design of neural networks, but the prohibitive amount of computations behind
current NAS methods requires further investigations in improving the sample
efficiency and the network evaluation cost to get better results in a shorter
time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS)
based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the
search efficiency by adaptively balancing the exploration and exploitation at
the state level, and by a Meta-Deep Neural Network (DNN) to predict network
accuracies for biasing the search toward a promising region. To amortize the
network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed
design and reduces the number of epochs in evaluating a network by transfer
learning guided with the tree structure in MCTS. In 12 GPU days and 1000
samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on
CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in
both the accuracy and sampling efficiency. Particularly, we also evaluate
AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more
sample efficient than Random Search and Regularized Evolution in finding the
global optimum. Finally, we show the searched architecture improves a variety
of vision applications from Neural Style Transfer, to Image Captioning and
Object Detection.Comment: another search algorithm for NAS. arXiv admin note: substantial text
overlap with arXiv:1805.0744
The entropy of lies: playing twenty questions with a liar
`Twenty questions' is a guessing game played by two players: Bob thinks of an
integer between and , and Alice's goal is to recover it using a minimal
number of Yes/No questions. Shannon's entropy has a natural interpretation in
this context. It characterizes the average number of questions used by an
optimal strategy in the distributional variant of the game: let be a
distribution over , then the average number of questions used by an
optimal strategy that recovers is between and .
We consider an extension of this game where at most questions can be
answered falsely. We extend the classical result by showing that an optimal
strategy uses roughly questions, where . This also generalizes a result by Rivest et
al. for the uniform distribution. Moreover, we design near optimal strategies
that only use comparison queries of the form `?' for . The
usage of comparison queries lends itself naturally to the context of sorting,
where we derive sorting algorithms in the presence of adversarial noise
Effects of Knowledge Base Quality on Peer-to-peer Information Propagation
Peer reviewedPublisher PD
Adapting Search Theory to Networks
The CSE is interested in the general problem of locating objects in networks. Because of their exposure to search theory, the problem they brought to the workshop was phrased in terms of adapting search theory to networks. Thus, the first step was the introduction of an already existing healthy literature on searching graphs.
T. D. Parsons, who was then at Pennsylvania State University, was approached in 1977 by some local spelunkers who asked his aid in optimizing a search for someone lost in a cave in Pennsylvania. Parsons quickly formulated the problem as a search problem in a graph. Subsequent papers led to two divergent problems. One problem dealt with searching under assumptions of fairly extensive information, while the other problem dealt with searching under assumptions of essentially zero information. These two topics are developed in the next two sections
Strategy in Ulam's Game and Tree Code Give Error-Resistant Protocols
We present a new approach to construction of protocols which are proof
against communication errors. The construction is based on a generalization of
the well known Ulam's game. We show equivalence between winning strategies in
this game and robust protocols for multi-party computation. We do not give any
complete theory. We want rather to describe a new fresh idea. We use a tree
code defined by Schulman. The tree code is the most important part of the
interactive version of Shannon's Coding Theorem proved by Schulman. He uses
probabilistic argument for the existence of a tree code without giving any
effective construction. We show another proof yielding a randomized
construction which in contrary to his proof almost surely gives a good code.
Moreover our construction uses much smaller alphabet.Comment: 10 pages, 2 figure
On Non-Parallelizable Deterministic Client Puzzle Scheme with Batch Verification Modes
A (computational) client puzzle scheme enables a client to prove to a server that a certain amount of computing resources (CPU cycles and/or Memory look-ups) has been dedicated to solve a puzzle. Researchers have identified a number of potential applications, such as constructing timed cryptography, fighting junk emails, and protecting critical infrastructure from DoS attacks. In this paper, we first revisit this concept and formally define two properties, namely deterministic computation and parallel computation resistance. Our analysis show that both properties are crucial for the effectiveness of client puzzle schemes in most application scenarios. We prove that the RSW client puzzle scheme, which is based on the repeated squaring technique, achieves both properties. Secondly, we introduce two batch verification modes for the RSW client puzzle scheme in order to improve the verification efficiency of the server, and investigate three methods for handling errors in batch verifications. Lastly, we show that client puzzle schemes can be integrated with reputation systems to further improve the effectiveness in practice
Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess
This paper proposes a new mechanism for pruning a search game-tree in
computer chess. The algorithm stores and then reuses chains or sequences of
moves, built up from previous searches. These move sequences have a built-in
forward-pruning mechanism that can radically reduce the search space. A typical
search process might retrieve a move from a Transposition Table, where the
decision of what move to retrieve would be based on the position itself. This
algorithm stores move sequences based on what previous sequences were better,
or caused cutoffs. This is therefore position independent and so it could also
be useful in games with imperfect information or uncertainty, where the whole
situation is not known at any one time. Over a small set of tests, the
algorithm was shown to clearly out-perform Transposition Tables, both in terms
of search reduction and game-play results.Comment: Publishe
Giraffe: Using Deep Reinforcement Learning to Play Chess
This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system also performs automatic feature extraction and pattern recognition. The
trained evaluation function performs comparably to the evaluation functions of
state-of-the-art chess engines - all of which containing thousands of lines of
carefully hand-crafted pattern recognizers, tuned over many years by both
computer chess experts and human chess masters. Giraffe is the most successful
attempt thus far at using end-to-end machine learning to play chess.Comment: MSc Dissertatio
Multi-owner Secure Encrypted Search Using Searching Adversarial Networks
Searchable symmetric encryption (SSE) for multi-owner model draws much
attention as it enables data users to perform searches over encrypted cloud
data outsourced by data owners. However, implementing secure and precise query,
efficient search and flexible dynamic system maintenance at the same time in
SSE remains a challenge. To address this, this paper proposes secure and
efficient multi-keyword ranked search over encrypted cloud data for multi-owner
model based on searching adversarial networks. We exploit searching adversarial
networks to achieve optimal pseudo-keyword padding, and obtain the optimal game
equilibrium for query precision and privacy protection strength. Maximum
likelihood search balanced tree is generated by probabilistic learning, which
achieves efficient search and brings the computational complexity close to
. In addition, we enable flexible dynamic system
maintenance with balanced index forest that makes full use of distributed
computing. Compared with previous works, our solution maintains query precision
above 95% while ensuring adequate privacy protection, and introduces low
overhead on computation, communication and storage.Comment: The 18th International Conference on Cryptology and Network Security.
Fixed minor issues with the conference version, such as spelling errors and
ambiguities in the content descriptio
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