The robot uses an abductive process, where sensor data is explaned by hypothesising the existence, location and shape of objects in the world. Symbols in the explanation are given meaning through the theory and are grounded by the robot interacting with the world. The project states its goal as generating a formal logic that will define the actions of a robot, allowing it to interact and learn about its environment. In the definition for Y, the predicting task is to work out Y - the sensed data. The paper goes on to introduce an 'event calculus' as a means of representing the logical states of the robot.
Events are instantaneous.
The benefit of having the robot interact with its world by using logic sentences generated from its inputs, is that it completely eliminates the frame problem. If extra objects are added or removed the robot will be at a loss as to what it should do. By utilising the formal logic described in this paper, any new objects that are encountered are instantly incorporated into the robot's description of the world, therefore it can cope with new objects or being immersed into a strange new environment. Initially the robot will think it can move anywhere because the initial map is empty, but as it bumps into objects it will correct its choices as to where to move next, eventually being able to avoid collisions. In terms of AI this feature is of great importance for robots that are required to interact with our everyday world.