Adjustably Autonomous Robotic Agents

Building autonomous robotic agents that interact with the real-world is a complex and difficult task and several distinct paradigms are used to develop such machines. In the past decade there has been an exploration of approaches inspired by evolutionary and/or neurodynamical principles. [1, 2, 3, 4]. In earlier work, we have examined architectures which support agent-based design of such robotic systems [5]. But, whilst there have been successes in developing systems that exhibit low levels of performance e.g. wall following, safe exploration, map building, it is proving more difficult to develop systems that succeed in high level tasks which depend upon machines being capable of exhibiting (many) different behaviours.