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Designing logic-based robots

A rational agent exploring a complex and dynamic environment with incomplete information needs cognitive capabilities, e.g. planning, in addition to its perception and reaction for basic functionalities. However, mere planning, i.e., reasoning about sequences of actions, is not sufficient to solve problems in such complex environment. This is because (i) agents need to execute actions while they plan, (ii) they must gather and interpret sensor information, (iii) revise their world model, and (iv) adapt their own goals during a task. The knowledge representation and non monotonic reasoning area has shown the advantage of using logical formalisms to specify rational agents for complex robot applications, also called cognitive robotics applications. An example of such formalism is the Golog language and its different dialects. What those areas are still missing is implementational testbeds to evaluate existing theories. Since practical experimentation highlights the need for the improvement of existing formal theories at the ontological level. This work presents a logic-based implementation of an agent for the Wumpus World domain, which can be envisaged as a simplified model of an agent that reasons logically about its actions and sensor information in the presence of incomplete knowledge. Through a complete implementation of an agent in a real robot, the LegoMindStorm robot, we show some experiments to verify that the Wumpus World can be an implementational testbed used to identify difficulties in transforming theory into operational solutions.