Argument-Driven Planning and Autonomous Explanation Generation

Research on general machine intelligence is concerned with building machines that are capable of performing a multitude of highly complex tasks in environments as complex as the real world. A system placed in such a world of indefinite possibilities and never-ending novelty must be able to adjust its plans dynamically to adapt to changes in the environment. These adjustments, however, should be based on an informed explanation that describes the hows and whys of interventions necessary to reach a goal. This means that explanations are at the core of planning in a self-explaining way. Using Assumption-Based Argumentation we present a way how an AGI-aspiring system could generate meaningful explanations. These explanations consist of argumentation graphs that represent proponents (i.e., solutions to the task) and opponents (contradictions to these solutions). They thus provide information on why which intervention is necessary, thus making an informed commitment to a particular action possible. Additionally, we show how such argumentation graphs could be used dynamically to adjust plans when contradicting evidence is observed from the environment.