scholarly journals Solving Sequential Text Classification as Board-Game Playing

2020 ◽  
Vol 34 (05) ◽  
pp. 8640-8648 ◽  
Author(s):  
Chen Qian ◽  
Fuli Feng ◽  
Lijie Wen ◽  
Zhenpeng Chen ◽  
Li Lin ◽  
...  
Keyword(s):  
Text Classification ◽  
Statistical Significance ◽  
Context Information ◽  
Game Playing ◽  
Board Game ◽  
Reading Order ◽  
Game State ◽  
Right Order ◽  
Novel Model ◽  
Label Sequence

Sequential Text Classification (STC) aims to classify a sequence of text fragments (e.g., words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the inter-fragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information first before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Specifically, we devise a dedicated board-game to develop a correspondence between solving STC and board-game playing. By defining proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to find the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The final game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical significance.

Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

2021 ◽  
Author(s):  
Alexander Dockhorn ◽  
Jorge Hurtado-Grueso ◽  
Dominik Jeurissen ◽  
Linjie Xu ◽  
Diego Perez-Liebana
Keyword(s):  
Monte Carlo ◽  
General Strategy ◽  
Tree Search ◽  
Game Playing ◽  
Monte Carlo Tree Search ◽  
Strategy Game ◽  
Game State

scholarly journals Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yifei Chen ◽  
Yuxing Sun ◽  
Bing-Qing Han
Keyword(s):  
Feature Selection ◽  
Protein Interaction ◽  
Text Classification ◽  
Protein Interactions ◽  
Reduction Rate ◽  
Importance Measure ◽  
Context Information ◽  
Selection Methods ◽  
Term Frequency ◽  
Context Similarity

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of theF1measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

scholarly journals Representing and Reasoning About the Rules of General Games With Imperfect Information

2014 ◽  
Vol 49 ◽  
pp. 171-206 ◽  
Author(s):  
S. Schiffel ◽  
M. Thielscher
Keyword(s):  
Imperfect Information ◽  
Complete Information ◽  
Game Playing ◽  
Description Language ◽  
General Game Playing ◽  
Game State ◽  
High Level ◽  
Game Description Language ◽  
General Game ◽  
Representation Formalism

A general game player is a system that can play previously unknown games just by being given their rules. For this purpose, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism to communicate game rules to players. In this paper, we address a fundamental limitation of state-of-the-art methods and systems for General Game Playing, namely, their being confined to deterministic games with complete information about the game state. We develop a simple yet expressive extension of standard GDL that allows for formalising the rules of arbitrary finite, n-player games with randomness and incomplete state knowledge. In the second part of the paper, we address the intricate reasoning challenge for general game-playing systems that comes with the new description language. We develop a full embedding of extended GDL into the Situation Calculus augmented by Scherl and Levesque's knowledge fluent. We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.

scholarly journals On Constructing Static Evaluation Function using Temporal Difference Learning

2013 ◽  
Vol 2 (1) ◽  
pp. 175-184
Author(s):  
Samuel Choi Ping Man
Keyword(s):  
Computer Game ◽  
Temporal Difference ◽  
Game Playing ◽  
Evaluation Function ◽  
Temporal Difference Learning ◽  
Board Games ◽  
Good Evaluation ◽  
Game State ◽  
Moderate Complexity ◽  
Static Evaluation

Programming computers to play board games against human players has long been used as a measure for the development of artificial intelligence. The standard approach for computer game playing is to search for the best move from a given game state by using minimax search with static evaluation function. The static evaluation function is critical to the game playing performance but its design often relies on human expert players. This paper discusses how temporal differences (TD) learning can be used to construct a static evaluation function through self-playing and evaluates the effects for various parameter settings. The game of Kalah, a non-chance game of moderate complexity, is chosen as a testbed. The empirical result shows that TD learning is particularly promising for constructing a good evaluation function for the end games and can substantially improve the overall game playing performance in learning the entire game.DOI: 10.18495/comengapp.21.175184

scholarly journals The time spent on board games pays off : links between board game playing and competency motivation

2021 ◽  
Vol 19 (1) ◽  
pp. 119-131
Author(s):  
András Ferenc Dukán ◽  
Katalin Fried ◽  
Csaba Szabó
Keyword(s):  
Game Playing ◽  
Board Game ◽  
Board Games

Board Games AI

2018 ◽  
pp. 144-155
Author(s):  
Tad Gonsalves
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Hard Problem ◽  
Game Playing ◽  
Evaluation Function ◽  
Board Game ◽  
Game Tree ◽  
Board Games ◽  
Np Hard Problem ◽  
Alpha Beta

The classical area of AI application is the board games. This chapter introduces the two most prominent AI approaches used in developing board game agents – the MinMax algorithm and Machine Learning and explains their usage in playing games like tic-tac-toe, checkers, othello, chess, go, etc., against human opponents. The game tree is essentially a directed graph, where the nodes represent the positions in the game and the edges the moves. Even a simple board game like tic-tac toe (noughts and crosses) has as many as 255,168 leaf nodes in the game tree. Traversing the complete game tree becomes an NP-hard problem. Alpha-beta pruning is used to estimate the short-cuts through the game tree. The board game strategy depends on the evaluation function, which is a heuristic indicating how good the player's current move is in winning the game. Machine learning algorithms try to evolve or learn the agent's game playing strategy based on the evaluation function.

Technical Elements of Machine Learning for Intellectual Property Law

2021 ◽  
pp. 11-27
Author(s):  
Anthony Man-Cho So
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Face Recognition ◽  
Intellectual Property ◽  
Speech Synthesis ◽  
Text Processing ◽  
Property Law ◽  
Game Playing ◽  
Board Game ◽  
Music Retrieval

Recent advances in artificial intelligence (AI) technologies have transformed our lives in profound ways. Indeed, AI has not only enabled machines to see (eg, face recognition), hear (eg, music retrieval), speak (eg, speech synthesis), and read (eg, text processing), but also, so it seems, given machines the ability to think (eg, board game-playing) and create (eg, artwork generation). This chapter introduces the key technical elements of machine learning (ML), which is a rapidly growing sub-field in AI and drives many of the aforementioned applications. The goal is to elucidate the ways human efforts are involved in the development of ML solutions, so as to facilitate legal discussions on intellectual property issues.

scholarly journals A Synthetic Player for Ayὸ Board Game Using Alpha-Beta Search and Learning Vector Quantization

2016 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Oluwatobi, A. Ayilara ◽  
Anuoluwapo, O. Ajayi ◽  
Kudirat, O. Jimoh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Vector Quantization ◽  
Search Algorithm ◽  
Learning Vector Quantization ◽  
Game Playing ◽  
Learning Approaches ◽  
Board Game ◽  
Significant Interest ◽  
Alpha Beta

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm.

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