Explore the words cloud of the GroundForce project. It provides you a very rough idea of what is the project "GroundForce" about.
The following table provides information about the project.
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE
|Coordinator Country||United Kingdom [UK]|
|Total cost||149˙867 €|
|EC max contribution||149˙867 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2016-10-01 to 2018-03-31|
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|1||THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE||UK (CAMBRIDGE)||coordinator||149˙867.00|
'While language is a natural way to interact with artificial characters or agents in video games, communication with agents currently tends to be limited to menu systems. To achieve smooth linguistic communication, utterances need to be grounded in the situation in which they occur. That is, the meanings of utterances must be learned from observing their use in some naturally occurring perceptual context. Recent years have seen much progress in the development of visually- or auditorily-grounded language understanding using novel machine learning techniques such as deep learning. At the same time, companies like Google DeepMind have introduced deep learning models that can learn to play games at super-human levels. We propose to take this research to the next step, by grounding natural language in video games.
Grounding natural language in video games yields two main benefits. The first benefit is commercial in nature: with the global market for video games expected to reach $100 billion by 2017, there is clearly a large demand for more sophisticated interaction with in-game agents. Secondly, video games are a natural way to explore artificial intelligence techniques in a 'simulated' world that is easier to understand computationally than the extremely complicated 'real' world.
The current project will explore natural language grounding in a small number of appropriate games. Once we are capable of grounding natural language in these games, we can translate utterances into straightforward actions for artificial agents. An example might be telling your team members to follow you, to take the left flank, or to duck when they are being shot at. Given the recent developments in machine learning and grounded language understanding, we believe that now is the perfect moment to explore these possibilities further. '
|year||authors and title||journal||last update|
Andrew J. Anderson, Douwe Kiela, Stephen Clark and Massimo Poesio
Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns
published pages: 17-30, ISSN: 2307-387X, DOI:
|Transactions of the Association for Computational Linguistics 5||2019-06-13|
Douwe Kiela, Anita Lilla VerÅ‘ and Stephen Clark
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics
published pages: 447-456, ISSN: , DOI:
|Proceedings of the Empirical Methods in Natural Language Processing Conference (EMNLP 2016)||2019-06-13|
Luana Bulat, Stephen Clark and Ekaterina Shutova
Speaking, Seeing, Understanding: Correlating Semantic Models with Conceptual Representation in the Brain
published pages: 1092-1102, ISSN: , DOI:
|Proceedings of the Empirical Methods in Natural Language Processing Conference (EMNLP 2017)||2019-06-13|
Douwe Kiela and Stephen Clark
Learning Neural Audio Embeddings for Grounded Semantics in Auditory Perception
published pages: 1003-1030, ISSN: 1076-9757, DOI:
|Journal of Artificial Intelligence Research 60||2019-06-13|
Luana Bulat, Stephen Clark and Ekaterina Shutova
Modelling Metaphor with Attribute-based Semantics
published pages: 523-528, ISSN: , DOI:
|Proceedings of the short papers of the 15th Conference of the European Chapter of the Association for Computational Linguistics||2019-06-13|
Douwe Kiela, Luana Bulat, Anita Lilla VerÅ‘ and Stephen Clark
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence research
published pages: , ISSN: , DOI:
|NIPS Workshop on Machine Intelligence (MAIN)||2019-06-13|
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