CAVAA

The Counterfactual Assessment and Valuation for Awareness Architecture (CAVAA) project proposes that awareness serves survival in a world governed by hidden states, to deal with the “invisible”, from unexplored environments to social interaction that depends on the internal states of agents and moral norms. Awareness reflects a virtual world, a hybrid of perceptual evidence, memory states, and inferred “unobservables”, extended in space and time. The CAVAA project will realize a theory of awareness instantiated as an integrated computational architecture and its components to explain awareness in biological systems and engineer it in technological ones. It will realize underlying computational components of perception, memory, virtualization, simulation, and integration, embody the architecture in robots and artificial agents, validate it across a range of use-cases involving the interaction between multiple humans and artificial agents, using accepted measures and behavioral correlates of awareness. Use cases will address robot foraging, social robotics, computer game benchmarks and human-generated decision trees in a health coach. These benchmarks will focus on resolving trade-offs, e.g. between search efficiency and robustness, and assess the acceptance of human users of aware technology. CAVAA’s awareness engineering is accompanied by an ethics framework towards human users and aware artefacts in the broader spectrum of trustworthy AI, considering shared ontologies, intention complementarity, and behavioral matching, an empathy, relevance of outcomes, reciprocity, counterfactuals and projections towards new future scenarios, and to predict the impact of choices. CAVAA will deliver a better user experience because of its explainability, adaptability, and legibility. CAVAA’s integrated framework redefines how we look at the relationship between humans, other species and smart technologies because it makes the invisible visible.

Our team was tasked with: 1) Research on self-supervised techniques for high-level computer vision and localization, 2) Research on unsupervised and open-vocabulary methods for object identification and segmentation in synthetic and natural scenes.

Consortium

STICHTING RADBOUD UNIVERSITEIT
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS
TECHNISCHE UNIVERSITAET CHEMNITZ
SORBONNE UNIVERSITE
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
EODYNE SYSTEMS SL
ROBOTNIK AUTOMATION SLL
UPPSALA UNIVERSITET
TP21 GMBH
THE UNIVERSITY OF SHEFFIELD
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD