Software Agents

Objectives

The group aims to advance the study and development of cognitive agents through an integrated approach that includes:

  • Conceptual Modeling and Process Simulation: Investigation of advanced techniques for representing and simulating cognitive, intelligence, and business processes, using agent-based approaches and artificial intelligence methods to model dynamic and realistic scenarios.
  • Knowledge Representation: Development and application of methods for ontology construction and formal concept analysis, enabling the systematization and inference of complex information in computational environments.
  • Computational System Architectures: Exploration of the multi-agent paradigm to create robust and adaptable systems capable of operating collaboratively and autonomously in dynamic and complex contexts.
  • LLM (Large Language Models) Techniques: Application of advanced language models to enhance the understanding, actions, and interactions of cognitive agents, enabling more accurate and context-aware responses.

Group Members

  • Edson Emílio Scalabrin, PhD
  • Fabrício Enembreck, PhD

Research Topics

  • Multi-Agent Systems and Cognitive Agents: Study and development of architectures and strategies for interaction and cooperation among autonomous agents, focusing on incorporating cognitive processes that enable sophisticated reasoning and decision-making.
  • Modeling and Simulation of Cognitive and Business Processes: Creation of models that represent and simulate both the cognitive aspects of intelligent systems and business process flows, allowing for behavioral analysis and optimization of complex systems.
  • Artificial Intelligence Applied to Agents: Application of AI techniques—such as machine learning, neural networks, and evolutionary algorithms—to enhance agents' ability to learn, adapt, and make decisions in dynamic environments.
  • Ontology Construction and Analysis: Development of semantic structures that organize and represent domain knowledge, facilitating inference and interoperability between systems and agents.
  • LLM (Large Language Models) Techniques and Applications: Investigation of large-scale language models to improve contextual understanding, response generation, and natural interaction between agents and users.
  • Distributed and Collaborative Architectures: Exploration of distributed system models that promote collaboration among multiple agents, ensuring scalability, robustness, and efficiency in executing complex tasks.
  • Coordination and Communication among Agents: Development of mechanisms and protocols that enable efficient communication and coordination of actions among agents, optimizing collaborative problem-solving.
  • Integration of Business Processes into Intelligent Systems: Application of methodologies and technologies to incorporate and automate business processes in intelligent systems, increasing operational efficiency and agility in decision-making.
  • Action Plan Generation from Business Process Models: Creation of frameworks that transform business process models into executable action plans, enabling autonomous and adaptive responses to business environment demands.