Title: DAMIAN – data accrual machine intelligence with augmented networks for contextually coherent creative story generation

Authors: B.J. Sowmya; D. Pradeep Kumar; R. Hanumantharaju; K.G. Srinivasa

Addresses: Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Affiliated to Visvesvaraya Technological University, Machhe, Belagavi, Karnataka 590018, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Affiliated to Visvesvaraya Technological University, Machhe, Belagavi, Karnataka 590018, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Affiliated to Visvesvaraya Technological University, Machhe, Belagavi, Karnataka 590018, India ' Department of Information Management and Coordination, National Institute of Technical Teachers Training and Research, Chandigarh-160019, India

Abstract: Cognitive computing refers to the usage of computer models to simulate human intelligence and thought process in a complex situation. Artificial intelligence (AI) is an augmentation to the limits of human capacity for a particular domain and works as an absolute reflection of reality. AI is where a computer program is able to efficiently make decisions without previous explicit knowledge and instruction. The concept of cognitive intelligence was introduced. The most interesting use case for this would be an AI bot that doubles as a digital assistant. This is aimed at solving core problems in AI like open domain question answering, context understanding, aspect-based sentiment analysis, text generation, etc. The work presents a model to develop a multi-resolution RNN to identify local and global context, develop contextual embedding via transformers to pass into a seq2seq architecture and add heavy regularisation and augment data with reinforcement learning, and optimise via recursive neural networks.

Keywords: cognitive computing; artificial intelligence; AI; data augmentation; human intelligence; recurrent neural network; transformer model.

DOI: 10.1504/IJBIDM.2023.127314

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.1/2, pp.115 - 130

Received: 31 Aug 2021
Accepted: 27 Dec 2021
Published online: 30 Nov 2022 *