MAIN ACHIEVEMENT:

1. 34 QUEST Cognitive ExoskeletonQualia Exploitation of Sensor Technology Fridays at noon can call into open discussions • MAIN ACHIEVEMENT: • Close the loop around the sensor and around the kill-chain, in a variety of applications, as relative concepts (qualia) and map them into plausible narratives (in subjective context) and manipulate (reason) with that representation to create a stable, useful and consistent world model (simulation). • HOW IT WORKS: • Instead of only using exemplars, prototypes or models for representing concepts (Sys1) we also use subjective knowledge of the environment to populate the representation (simulation – Sys2) capturing the relative characteristics (qualia). QUEST solutions consist of an autistic Sys1 and a quale generating kernel and a set of processes to manipulate / reason / deliberate with that Sys2 representation. The Sys2 representation is characterized by gists and linksand a mix of basic agents with some quest agents. • ASSUMPTIONS AND LIMITATIONS: • Link based representation where the links have hierarchical relationships can represent any concept in subjective context = situations. • There exists a universal kernel for extracting the qualia(subjective concept representation). • Common framework for integrating human and machine QUEST agents possible. • Within an application performance improvements will be demonstrated and performance bounding will be possible via a Theory of Knowledge. •The ability to extend the solution to unexpected queries. STATUS QUO • Computer aides are limited to well defined environments and only for single applications. • Variations in application or location in kill-chain (AIFIFT2EA4)require re-engineering the solution. • •Solutions are reflexive/‘autistic’, with no ability to learn concepts in context (situations), adapt to operating conditions or bound performance (DDAI) QUANTITATIVE IMPACT • Input is never considered without context (subjective representation of the situation) and only has value relative to prior, current or expected experiences (qualia) • Intuition + Deliberation SoS representation optimized for exploitation versus fidelity with physical reality required for intelligence. • • Interaction with the environment is critical. What are the key new insights? (REPLACETHIS BOX AND INSERT DIAGRAM(S)) END-OF-PHASE GOAL NEW INSIGHTS Exploitation in a layered sensing environment •Computation of ‘self’ and inclusion in world model. •A general Theory of Mind facilitating anticipation / alignment. An integrated human/computer solution with the ability to capture and understand situations around the kill chain will offer a dramatic engineering advantage over current ‘autistic’ solutions.