Autonomous Machine Learning What kind of a-priori knowledge do we have to provide to our systems for showing such a capability? Edgar Koerner Honda Research Institute Europe Offenbach, Germany

WCCI Barcelona 2 Prerequisites for autonomous knowledge acquisition Autonomous learning, learning while behaving –Learner is the subject, defines by itself what to learn Requires subjective knowledge representation Behaviour provides semantics to data that the system is in a position to define what to learn and where in the system to learn Capability to associate current situation with acquired experience to define what is new, and in which relation to the already acquired knowledge it should be memorized –Living beings are still the only example of autonomous learners in complex environments Living beings without cortex: autonomous systems (reflex automatons) genetically encoded reflex hierarchy genetically encoded “value system” = mapping of sensory prototypic situations to behavioural prototypes Living beings with cortex: flexible autonomous systems (learning systems) reflex hierarchy value system + memory system (cortex) mapping sensory situation to behaviour control, predominantly genetically determined + self-referential control (also genetically encoded) to enable the system to generate a consistent relational architecture of the knowledge representation

WCCI Barcelona 3 Self-referential control is the key for autonomous learning Self-referential control may serve as a “representational immune system” An autonomous learning system must have the capability to decompose its sensory input according to its already acquired knowledge For an autonomous system, the only way to “understand” input is to relate it to already stored experience (association) If the complete input cannot be closely matched to existing experience, the “width of association” must be controlled in a way to enable both coarse sorting for behavioral categorization, and discrimination of the new aspects Activating the next nearest existing representation anchors the situation in the semantics of behavioural relevance Subsequent iterative decomposition of the sensory input by existing representations can create a cumulative extension linked at the anchor point for integration into the already acquired subjective knowledge representation Understanding the sensory input by decomposing it into existing representational elements and remaining new aspects (residual) ensures Consistent relational structure of knowledge representation Efficient representation Isolating the new → selective attention Incrementally building up and correcting knowledge representation Basis for internal simulation Active disambiguation in complex situations, etc.