ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample physical space are barriers that limit access to robotics experiences for all learners. We propose ARtonomous, a relatively low-cost, virtual alternative to physical, programming-only robotics kits. With ARtonomous, students employ reinforcement learning (RL) alongside code to train and customize virtual autonomous robotic vehicles. Through a study evaluating ARtonomous, we found that middle-school students developed an understanding of RL, reported high levels of engagement, and demonstrated curiosity for learning more about ML. This research demonstrates the feasibility of an approach like ARtonomous for 1) eliminating barriers to robotics education and 2) promoting student learning and interest in RL and ML.Comment: In Proceedings of Interaction Design and Children (IDC '22

Drones in Extension Programming: Implementation of Adult and Youth Activities

The use of unmanned aircraft systems (UASs), or consumer drones, in agriculture has the potential to revolutionize the way certain farm practices are conducted and the way science, technology, engineering, and math principles can be taught. Currently, there is need for UAS training for both adults and youths, and that need will increase with the expected growth of the UAS industry. This article addresses the need to include UASs in Extension programming, the associated legalities, and the best types of UASs to use in such programming

Argumentative systems in robots

Nowadays, the development of intelligent agents intends to be more refined, using improved architectures and reasoning mechanisms. Revise the beliefs of an agent is also an important subject, due to the consistency that agents should have about their knowledge. In this work we propose deliberative and argumentative agents using Lego Mindstorms robots, Argumentative NXT BDI-like Agents. These agents are built using the notions of the BDI model and they are capable to reason using the DeLP formalism. They update their knowledge base with their perceptions and revise it when necessary. Two variations are presented: the Single Argumentative NXT BDI-like Agent and the MAS Argumentative NXT BDI-like Agent.Universidade da Madeir

Multi-robotic teamwork in AgentSpeak

Within the multiagent community, agent programming languages and theories of agent teamwork are used together to create teams of cooperative agents to achieve goals in dynamic environments. Multi-robotic research studies robots working together in a team to perform a task and achieve a goal. In this thesis we propose a set of programming constraints for multi-robotic cooperation. We then use these programming constraints to build a multi-robotic cooperation framework. This framework is a bridge between multiagent programming languages and multi-robotic teamwork. The framework uses the natural and intuitive programming style that AgentSpeak provides for multiagent cooper ation to create multi-robotic teams. We then use a group of LEGO® NXT 2.0 robots to test the proposed programming constraints, implemented in AgentSpeak, to operate a search and rescue scenario