Search Results for author: Don Joven Agravante

Found 10 papers, 3 papers with code

Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning

no code implementations2 Feb 2024 Debarun Bhattacharjya, JunKyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu

Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

no code implementations6 Jun 2023 Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori

The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.

Hierarchical Reinforcement Learning with AI Planning Models

1 code implementation1 Mar 2022 JunKyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, Shirin Sohrabi

Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).

Decision Making Hierarchical Reinforcement Learning +2

LOA: Logical Optimal Actions for Text-based Interaction Games

1 code implementation ACL 2021 Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray

We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.

reinforcement-learning Reinforcement Learning (RL) +1

Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization

no code implementations10 Dec 2020 Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana

Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts.

Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization

no code implementations10 Dec 2020 Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Phongtharin Vinayavekhin

This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources.

Domain Adaptation

Constrained Exploration and Recovery from Experience Shaping

1 code implementation21 Sep 2018 Tu-Hoa Pham, Giovanni De Magistris, Don Joven Agravante, Subhajit Chaudhury, Asim Munawar, Ryuki Tachibana

We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties.

reinforcement-learning Reinforcement Learning (RL)

Cannot find the paper you are looking for? You can Submit a new open access paper.