We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/tops.12573 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning.
Adv Sci (Weinh)
January 2025
School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
3D disordered fibrous network structures (3D-DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure-property relationships and stability of biomimetic 3D-DFNS using large datasets generated through procedural modeling, coarse-grained molecular dynamics simulations, and machine learning.
View Article and Find Full Text PDFLearning and fine-tuning a generic value-selection heuristic inside a constraint programming solver.
Constraints
November 2024
Polytechnique Montréal, Montreal, Canada.
Constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the , designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise.
View Article and Find Full Text PDFRL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning.
Front Neurorobot
January 2025
Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi, Henan, China.
Introduction: Path planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. These methods have high computational costs and are not efficient for real-time applications.
View Article and Find Full Text PDFAn oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer.
J Oral Microbiol
January 2025
Integrative Microecology Clinical Center, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, The Clinical Innovation & Research Center, Shenzhen Key Laboratory of Viral Oncology, Department of Clinical Nutrition, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Background: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.
Methods: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.
Effectiveness of Various Sternal Closure Devices Post Adult Cardiac Surgery.
Heart Lung Circ
January 2025
Department of Cardiothoracic Surgery, Royal Melbourne Hospital, Melbourne, Vic, Australia.
Background & Aim: Sternotomy remains a commonly used technique to access the heart for cardiac surgery worldwide. To date, there is no clear consensus on the single superior sternal closure technique. Patient-specific factors such as osteoporosis, diabetes, old age, body habitus influence a surgeon's choice in this matter as do techniques commonly used during the training period and used in the current workplace.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!