Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable Artificial Intelligence

Open Access
Article
Conference Proceedings
Authors: Eric DavisSourya DeyAdam KarvonenEthan LewDonya QuickPanchapakesan ShyamshankarTed HilleMatt Lebeau

Abstract: Improvements in neural methods have led to the unprecedented adoption of AI in domains previously limited to human experts. As these technologies mature, especially in the area of neuro-symbolic intelligence, interest has increased in artificial cognitive capabilities that would allow an AI system to function less like an application and more like an interdependent teammate. In addition to improving language capabilities, next-generation AI systems need to support symbiotic, human-centered processes, including objective alignment, trust calibration, common ground, and the ability to build complex workflows that manage risks due to resources such as time, environmental constraints, and diverse computational settings from super computers to autonomous vehicles.In this paper we review current challenges in achieving Symbiotic Intelligence, and introduce novel capabilities in Artificial Executive Function we have developed towards solving these challenges. We present our work in the context of current literature on context-aware and self-aware computing and present basic building blocks of a novel, open-source, AI architecture for Symbiotic Intelligence. Our methods have been demonstrated effectively in both simulated crisis and during the pandemic. We argue our system meets the basic criteria outlined by DARPA and AFRL providing: (1) introspection via graph-based reasoning to establish expectations for both autonomous and team performance, to communicate expectations for interdependent co-performance, capability, an understanding of shared goals; (2) adaptivity through the use of automatic workflow generation using semantic labels to understand requirements, constraints, and expectations; (3) self healing capabilities using after-action review and co-training capabilities; (4) goal oriented reasoning via an awareness of machine, human, and team responsibilities and goals; (5) approximate, risk-aware, planning using a flexible workflow infrastructure with interchangeable units of computation capable of supporting both high fidelity, costly, reasoning suitable for traditional data centers, as well as in-the-field reasoning with highly performable surrogate models suitable for more constrained edge computing environments. Our framework provides unique symbiotic reasoning to support crisis response, allowing fast, flexible, analysis pipelines that can be responsive to changing resource and risk conditions in the field. We discuss the theory behind our methods, practical concerns, and our experimental results that provide evidence of their efficacy, especially in crisis decision making.

Keywords: symbiosis, human, machine teaming, human, robot interaction, interdependence, artificial cognition

DOI: 10.54941/ahfe1003759

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