Adaptive joint cognitive systems for complex and strategic decision making: building trust in human-machine teams through brain-computer-interface augmentation, social interaction and mutual learning
Abstract
In this ambitious project, we will develop a novel architecture for complex group decision making that integrates in an unprecedented way the strengths of human and AI team members , while compensating for their respective weaknesses . Our approach builds on many years of highly interdisciplinary research from our team on group decision making assisted by Brain-Computer Interfaces (BCIs) in human and human-machine teams , as well as state of the art machine-learning technology, neuroscience, human-factors and psychophysiologic knowledge on decision making in humans and human teams. Our architecture leverages three core AI elements to extend the capabilities of human decision makers: virtual humans, virtual personal assistants and virtual team assistants. Virtual humans are artificial agents based on advanced AI and machine-learning techniques effectively behaving like equal team members to the humans. They can make their own decisions in the task at hand, and, like humans, evaluate and communicate their degree of confidence in those decisions, provide explanations for their decisions if asked, learn to trust and become trusted, and understand when their performance is degrading and they need to abstain. Virtual personal assistants are artificial agents that incorporate BCI technology , advanced neuroscience and neural engineering to collect neural, physiological (e.g., skin conductance), and behavioral (e.g., decision time) information from a human decision maker. They use this input to estimate the probability of each decision being correct ( objective confidence ) and recognise states (e.g., error-related potentials in the brain) that indicate mistakes and changes of mind by the human operator as well as negative reactions to other team membersÕ decisions. The virtual team assistant is an artificial agent that monitors behaviours, performance and mutual trust of all team members as well as verbal and non-verbal communication in humans to optimally integrate their decisions into team decisions, while constantly guiding the social interaction within the team for the purpose of optimising performance and teamwork (e.g., building mutual trust). To address the challenges posed by the project we have assembled a multidisciplinary team with expertise in AI, machine learning, neural engineering, computer science, neuroscience and cognitive psychology . We will take a fully integrated approach by combining rare state-of-the-art experimental capabilities with novel computational modeling and AI technology . Complementary behavioural and neuroimaging research (EEG, fMRI and ECoG) will collect data to study and model key elements of decision making, including decision confidence, trust, performance feedback, attention and emotions, and will guide the development of BCIs, AI trust models, and humanised forms of machine learning capable of expressing confidence and providing explanations.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 14, 2019
- Source ID
- W911NF1810434
Entities
People
- Maryam Shanechi
Organizations
- Army Contracting Command
- Office of the Secretary of Defense
- University of Southern California