88,246 research outputs found
Understanding Computer Role-Playing Games: A Genre Analysis Based on Gameplay Features in Combat Systems
A game genre as diverse as that of computer role-playing games is difficult to overview. This poses challenges or both developers and researchers to position their work clearly within the genre. We present an overview of the genre based on clustering games with similar gameplay features. This allows a tracing of relations between subgenres through their gameplay, and connecting this to concrete game examples. The analysis was done through using gameplay design patterns to identify gameplay features and focused upon the combat systems in the games. The resulting cluster structure makes use of 321 patterns to create 37 different subgenre classifications based solely on gameplay features. In addition to the clusters, we identify four categories of patterns that help designers and researchers understand the combat systems in computer role-playing games
Event Representations for Automated Story Generation with Deep Neural Nets
Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language models at character, word, or sentence levels have had little success
generating coherent stories. We explore the question of event representations
that provide a mid-level of abstraction between words and sentences in order to
retain the semantic information of the original data while minimizing event
sparsity. We present a technique for preprocessing textual story data into
event sequences. We then present a technique for automated story generation
whereby we decompose the problem into the generation of successive events
(event2event) and the generation of natural language sentences from events
(event2sentence). We give empirical results comparing different event
representations and their effects on event successor generation and the
translation of events to natural language.Comment: Submitted to AAAI'1
Three Approaches to Generating Texts in Different Styles
Natural Language Generation (nlg) systems generate texts in English and other human languages from non-linguistic input data. Usually there are a large number of possible texts that can communicate the input data, and nlg systems must choose one of these. We argue that style can be used by nlg systems to choose between possible texts, and explore how this can be done by (1) explicit stylistic parameters, (2) imitating a genre style, and (3) imitating an individual’s style
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Goal-based structuring in a recommender systems
Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using recommendations) taking into account the users' immediate interests: a goal-based structuring method. Goal-based structuring is based on the fact that people experience certain gratifications from using information, which should match with their goals. An experiment using an electronic TV guide shows that structuring information using a goal-based structure makes it easier for users to find interesting information, especially if the goals are used explicitly; this is independent of whether recommendations are used or not. It also shows that goal-based structuring has more influence on how easy it is for users to find interesting information than recommendations
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