Call for Papers

The ACM Symposium on Applied Computing (SAC) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2024 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Avila, Spain. In SAC 2015, the technical track on Intelligent Robotics and Multi-Agent Systems (IRMAS) joined the former tracks on Intelligent Robotic Systems (ROBOT) and Cooperative Multi-Agent Systems and Applications (CMASA) which had been organized as separate technical tracks in past editions of SAC. The IRMAS track will be organized for the tenth time in SAC 2024, exploiting the inherent synergy between Robotics and Multi-Agent Systems (MAS), thus aiming to bring together these highly related and exciting research fields.

Robotics is a multidisciplinary research area that presents an enormous potential. It concerns about developing intelligent robotic systems that are capable of making decisions and acting autonomously in real and unpredictable environments to accomplish tasks and assist humans in relevant application domains for society. Several complex problems require the use of teams of robots that share some of the same challenges studied in MAS.

MAS are groups of intelligent agents that can perceive and act in a given environment to achieve their individual and collective goals. MAS enable solving problems that go beyond the individual capabilities and knowledge of single agents, not suffering from resource limitations, performance bottlenecks, or critical failures usually found in centralized problem solvers. Multi-robot systems are often used to evaluate and validate MAS with physical robot platforms.

For many years, Robotics and Artificial Intelligence (AI) researchers have worked separately in these fields, both fields have matured enormously, and there has been a growing interest in getting the two fields together for the past decade. Many in Robotics believe that the focus in the near future should be adding capabilities to robots that lie at the core of AI research. Reciprocally, AI researchers aim at embedding their techniques in physical robots that can perceive, reason and act in real, dynamic physical environments. Despite this mutual interest, although there are many conferences focusing either on Robotics or AI separately, there is still a lack of scientific venues where both communities can meet. 

The purpose of the IRMAS track is therefore to provide a venue to exploit synergies between Robotics and AI, more precisely between Intelligent Robotics and MAS, bringing together researchers from both fields to share experiences, expose issues, and discuss about these exciting fields. Papers that make fundamental contributions in either of these two areas are welcome, and research that spans both areas is especially encouraged.

Accepted papers will be included in the ACM SAC 2024 proceedings and published in the ACM digital library, being indexed by Thomson ISI Web of Knowledge and ScopusTop-quality papers will be possibly invited after the conference for submission, in extended form, to a special issue of a ISI indexed journal.

Topics of Interest

      Autonomous robots         Robot localization, mapping and navigation
      Multi-agent systems (MAS) theory     Artificial perception and computer vision
      Cooperative robots and MAS     Field robotics
      Multi-robot systems     Cooperative perception
      Coordination and cooperation     Deployment, coverage and patrolling
      Decentralized control architectures     Evolutionary robotics and swarm robotics
      Human-robot interaction     Social and service robotics
      Collaborative robots     Entertainment and educational robots
      Real-world applications of MAS     Microrobotics and nanorobotics
      Self-adaptation and learning     Robotic dexterous grasping
      MAS in mobile ad hoc sensor networks     Simulation and programming tools for MAS


Download the Call for Papers of SAC 2024 IRMAS track in PDF