The issue of explainability for autonomous systems is becoming increasingly prominent. Several researchers and organisations have advocated the provision of a “Why did you do that?” button which allows a user to interrogate a robot about its choices and actions. We take previous work on debugging cognitive agent programs and apply it to the question of supplying explanations to end users in the form of answers to why-questions. These previous approaches are based on the generation of a trace of events in the execution of the program and then answering why-questions using the trace. We implemented this framework in the agent infrastructure layer and, in particular, the Gwendolen programming language it supports – extending it in the process to handle the generation of applicable plans and multiple intentions. In order to make the answers to why-questions comprehensible to end users we advocate a two step process in which first a representation of an explanation is created and this is subsequently converted into natural language in a way which abstracts away from some events in the trace and employs application specific predicate dictionaries in order to translate the first-order logic presentation of concepts within the cognitive agent program in natural language. A prototype implementation of these ideas is provided.

Autonomous systems are increasingly being used in safety-and mission-critical domains, including aviation, manufacturing, healthcare and the automotive industry. Systems for such domains are often verified with respect to essential requirements set by a regulator, as part of a process called certification. In principle, autonomous systems can be deployed if they can be certified for use. However, certification is especially challenging as the condition of both the system and its environment will surely change, limiting the effective use of the system. In this paper we discuss the technological and regulatory background for such systems, and introduce an architectural framework that supports verifiably-correct dynamic self-certification by the system, potentially allowing deployed systems to operate more safely and effectively.