6,362 research outputs found
Realistic Dialogue Engine for Video Games
The concept of believable agent has a long history in Artificial Intelligence. It has applicability in multiple fields, particularly video games. Video games have shown tremendous technological advancement in several areas such as graphics and music; however, techniques used to simulate dialogue are still quite outdated. In this thesis, a method is proposed to allow a human player to interact with non-player characters using natural-language input. By using various techniques of modern Artificial Intelligence such as information retrieval and sentiment analysis, non-player characters have the capability of engaging in dynamic dialogue: they can answer questions, ask questions, remember events, and more. This conversation system is highly customizable, so the types of responses that non-player characters give can be modified to fit within a game’s storyline. Although the system only currently allows for simple dialogue, it illustrates the potential for a more robust way to simulate believable agents in video games
How software engineering research aligns with design science: A review
Background: Assessing and communicating software engineering research can be
challenging. Design science is recognized as an appropriate research paradigm
for applied research but is seldom referred to in software engineering.
Applying the design science lens to software engineering research may improve
the assessment and communication of research contributions. Aim: The aim of
this study is 1) to understand whether the design science lens helps summarize
and assess software engineering research contributions, and 2) to characterize
different types of design science contributions in the software engineering
literature. Method: In previous research, we developed a visual abstract
template, summarizing the core constructs of the design science paradigm. In
this study, we use this template in a review of a set of 38 top software
engineering publications to extract and analyze their design science
contributions. Results: We identified five clusters of papers, classifying them
according to their alignment with the design science paradigm. Conclusions: The
design science lens helps emphasize the theoretical contribution of research
output---in terms of technological rules---and reflect on the practical
relevance, novelty, and rigor of the rules proposed by the research.Comment: 32 pages, 10 figure
Recommended from our members
Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods
Programming languages and platforms improve over time, sometimes resulting in
new language features that offer many benefits. However, despite these
benefits, developers may not always be willing to adopt them in their projects
for various reasons. In this paper, we describe an empirical study where we
assess the adoption of a particular new language feature. Studying how
developers use (or do not use) new language features is important in
programming language research and engineering because it gives designers
insight into the usability of the language to create meaning programs in that
language. This knowledge, in turn, can drive future innovations in the area.
Here, we explore Java 8 default methods, which allow interfaces to contain
(instance) method implementations.
Default methods can ease interface evolution, make certain ubiquitous design
patterns redundant, and improve both modularity and maintainability. A focus of
this work is to discover, through a scientific approach and a novel technique,
situations where developers found these constructs useful and where they did
not, and the reasons for each. Although several studies center around assessing
new language features, to the best of our knowledge, this kind of construct has
not been previously considered.
Despite their benefits, we found that developers did not adopt default
methods in all situations. Our study consisted of submitting pull requests
introducing the language feature to 19 real-world, open source Java projects
without altering original program semantics. This novel assessment technique is
proactive in that the adoption was driven by an automatic refactoring approach
rather than waiting for developers to discover and integrate the feature
themselves. In this way, we set forth best practices and patterns of using the
language feature effectively earlier rather than later and are able to possibly
guide (near) future language evolution. We foresee this technique to be useful
in assessing other new language features, design patterns, and other
programming idioms
Ontology-based Why-Question Analysis Using Lexico-Syntactic Patterns
This research focuses on developing a method to analyze why-questions. Some previous researches on the why-question analysis usually used the morphological and the syntactical approach without considering the expected answer types. Moreover, they rarely involved domain ontology to capture the semantic or conceptualization of the content. Consequently, some semantic mismatches occurred and then resulting not appropriate answers. The proposed method considers the expected answer types and involves domain ontology. It adapts the simple, the bag-of-words like model, by using semantic entities (i.e., concepts/entities and relations) instead of words to represent a query. The proposed method expands the question by adding the additional semantic entities got by executing the constructed SPARQL query of the why-question over the domain ontology. The major contribution of this research is in developing an ontology-based why-question analysis method by considering the expected answer types. Some experiments have been conducted to evaluate each phase of the proposed method. The results show good performance for all performance measures used (i.e., precision, recall, undergeneration, and overgeneration). Furthermore, comparison against two baseline methods, the keyword-based ones (i.e., the term-based and the phrase-based method), shows that the proposed method obtained better performance results in terms of MRR and P@10 values
- …