SYNERGY

Scope

SYNERGY is a workshop that aims at fostering in-depth discussions on Knowledge Representation (KR) and neuro-symbolic architectures with the goal of advancing intelligent robotics to new levels of sophistication.

Integrating robust KR-based methodologies into robot-centric architectures holds significant promise for enhancing the intelligence of robotic systems. However, the development of such architectures is complex, typically involving diverse components each with unique challenges. Effective coordination among these components is essential, demanding well-defined yet flexible architectures.

Recent advancements in machine learning (ML) and large-language models (LLMs) have brought opportunities for transformative breakthroughs in robotics through neuro-symbolic architectures. However, along with these opportunities come notable challenges. For instance, LLMs can facilitate smoother human-machine interfaces and serve as repositories of common-sense knowledge. Nonetheless, their susceptibility to prompt phrasing and tendency to generate erroneous outputs pose substantial hurdles, particularly in mission-critical and safety-critical applications like robotics.

SYNERGY offers a platform for experts across these domains to share their research and experiences, focusing on the challenges encountered and potential solutions. While the workshop centers on KR-based architectures, we also invite submissions related to ML and LLMs, provided they clearly demonstrate relevance and significance to KR and neuro-symbolic architectures within the context of intelligent robotics.

Program (November 3rd, 2024)

Here the Zoom Link to follow SYNERGY remotely: https://yorku.zoom.us/j/98334736209?pwd=BSu5pb7vbY7bXRh5QCGbtFbugWwTjY.1

Audience

SYNERGY aims to provide a collaborative platform for experts working in knowledge representation, neuro-symbolic architectures, machine learning, and robotics to exchange ideas, discuss challenges, and explore innovative solutions. We encourage submissions that demonstrate clear connections to KR and neuro-symbolic architectures, even if the primary focus is on ML or LLMs.

The topics of interest for SYNERGY include, but are not limited to:

• Knowledge representation frameworks for intelligent robotics

• Neuro-symbolic architectures and their applications in robotics

• Coordination and integration of multiple components in robot-focused architectures

• Machine learning (ML) techniques and large-language models (LLMs) in the context of robotics

• Interfaces and interactions between robots and humans enabled by ML and LLMs

• Challenges and solutions in using ML and LLMs for robotics, with emphasis on safety and reliability

Organization

Program Co-Chairs:

• Francesco Fabiano, New Mexico State University

• Marcello Balduccini, Saint Joseph's University

Local Chair:

• Yves Lesperance, York University

Program Committee

• Daniela Inclezan, Miami University

• Esra Erdem, Sabanci University

• Fabio Tardivo, New Mexico State University 

• Francesco Ricca, University of Calabria

• Gopal Gupta, University of Texas at Dallas

• Mario Alviano, University of Calabria

• Son Tran, New Mexico State University