Wyniki wyszukiwania - BazTech

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Voice Controlled Games – The approach and challenges of implementing speech recognition and voice control in games

Strzałko Dominik

Annals of Computer Science and Information Systems

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2021

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Vol. 26

229--230

EN

The subject of voice controlled games is quite underrated and exciting. Even though there are not that many papers focusing on that particular subject, we can find many papers describing both, concept of voice recognition, and controlling the game using natural language. The most illustrative example of usage of that kind of control are games in VR/AR/MR and games for people with disabilities. Moreover, almost every game can benefit from voice commands e.g. for controlling the user interface or units in strategy game. Therefore, voice controlled games are an interesting and innovative concept for game designers and developers.

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An English neural network that learns texts, finds hidden knowledge, and answers questions

Ke Y., Hagiwara M.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 4

229--242

EN

In this paper, a novel neural network is proposed, which can automatically learn and recall contents from texts, and answer questions about the contents in either a large corpus or a short piece of text. The proposed neural network combines parse trees, semantic networks, and inference models. It contains layers corresponding to sentences, clauses, phrases, words and synonym sets. The neurons in the phrase-layer and the word-layer are labeled with their part-of-speeches and their semantic roles. The proposed neural network is automatically organized to represent the contents in a given text. Its carefully designed structure and algorithms make it able to take advantage of the labels and neurons of synonym sets to build the relationship between the sentences about similar things. The experiments show that the proposed neural network with the labels and the synonym sets has the better performance than the others that do not have the labels or the synonym sets while the other parts and the algorithms are the same. The proposed neural network also shows its ability to tolerate noise, to answer factoid questions, and to solve single-choice questions in an exercise book for non-native English learners in the experiments.

3

Modeling Variations of First-Order Horn Abduction in Answer Set Programming

Schüller P.

Fundamenta Informaticae

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2016

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Vol. 149, nr 1/2

159--207

EN

We study abduction in First Order Horn logic theories where all atoms can be abduced and we are looking for preferred solutions with respect to three objective functions: cardinality minimality, coherence, and weighted abduction. We represent this reasoning problem in Answer Set Programming (ASP), in order to obtain a flexible framework for experimenting with global constraints and objective functions, and to test the boundaries of what is possible with ASP. Realizing this problem in ASP is challenging as it requires value invention and equivalence between certain constants, because the Unique Names Assumption does not hold in general. To permit reasoning in cyclic theories, we formally describe fine-grained variations of limiting Skolemization. We identify term equivalence as a main instantiation bottleneck, and improve the efficiency of our approach with on-demand constraints that were used to eliminate the same bottleneck in state-of-the-art solvers. We evaluate our approach experimentally on the ACCEL benchmark for plan recognition in Natural Language Understanding. Our encodings are publicly available, modular, and our approach is more efficient than state-of-the-art solvers on the ACCEL benchmark.

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Natural Language Understanding and Prediction: from Formal Grammars to Large Scale Machine Learning

Duta N.

Fundamenta Informaticae

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2014

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Vol. 131, nr 3-4

425--440

EN

Scientists have long dreamed of creating machines humans could interact with by voice. Although one no longer believes Turing's prophecy that machines will be able to converse like humans in the near future, real progress has been made in the voice and text-based human-machine interaction. This paper is a light introduction and survey of some deployed natural language systems and technologies and their historical evolution. We review two fundamental problems involving natural language: the language prediction problem and the language understanding problem. While describing in detail all these technologies is beyond our scope, we do comment on some aspects less discussed in the literature such as language prediction using huge models and semantic labeling using Marcus contextual grammars.