Improving social competences of virtual agents through artificial consciousness based on the Attention Schema Theory.
ASTOUND proposes an Integrative Approach For Awareness Engineering to establish consciousness in machines. The approach consists of an AI architecture for Artificial Consciousness based on the Attention Schema Theory (AST), a novel approach to social cognition that reconciles some of the current most debated cognitive neuroscience theories of consciousness. According to the AST, the brain constructs subjective awareness as a schematic model of the process of attention, suggesting that an information-processing machine could attribute consciousness properties to others in a similar way. The AST-based architecture proposed by ASTOUND will combine an Attention Mechanism provided by the attentional layers in a deep neural architecture and a Long Term Memory module allowing interplay between internal and external stimuli (data) with an Attention Schema that will determine empathic and trustworthy decision-making.
Counterfactual Assessment and Valuation for Awareness Architecture
The 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 behavioural correlates of awareness.
Emergent Awareness from Minimal Collectives
When intelligence is distributed across many parts, be they robots, devices, or objects, it can be tricky for the bigger picture to emerge. Yet answering these questions is key to making collective systems that are easy to design, monitor and control.
EMERGE will deliver a new philosophical, mathematical, and technological framework to demonstrate, both theoretically and experimentally, how a collaborative awareness – a representation of shared existence, environment and goals – can arise from the interactions of elemental artificial entities.
In this effort, we will rely only on unstructured conditions that the real world demands without leveraging a pre-existing shared language between them. Our goal is to surpass the limitations and barriers of the current state-of-the-art distributed systems to produce breakthroughs and open new markets in the next generation of robotic systems.
A metapredictive model of synthetic awareness for enabling tool invention
METATOOL aims to provide a computational model of synthetic awareness to enhance adaptation and achieve tool invention. This will enable a robot to monitor and self-evaluate its performance, ground and reuse this information for adapting to new circumstances, and finally unlock the possibility of creating new tools.Under the predictive account of awareness, and based on both neuroscientific and archeological evidence, we will: 1) develop a novel computational model of metacognition based on predictive processing (metaprediction) and 2) validate its utility in real robots in two use case scenarios: conditional sequential tasks and tool creation. METATOOL will provide a blueprint for the next generation of artificial systems and robots that can perform adaptive, and anticipative, control with and without tools (improved technology), self-evaluation (novel explainable AI), and invent new tools (disruptive innovation).
Smart Building Sensitive to Daily Sentiment
SUST(AI)N derives theoretical & experimental underpinnings to combine novel distributed intelligence, unprecedented sensing accuracy, and reconfigurable hardware in a smart building context into a conscious organism that achieves self-awareness through probabilistic reasoning across its connected sustainable devices. SUST(AI)N constitutes the first concentrated effort to explore novel advances in distributed intelligence, reconfigurable hardware, and environmental sensing to establish awareness for smart buildings that reaches global availability of information (C11; through data aggregation across connected reconfigurable hardware), and self-monitoring (C21; via distributed probabilistic intelligence and the sensing of group sentiment).
Symbolic logic framework for situational awareness in mixed autonomy
SymAware addresses the fundamental need for a new conceptual framework for awareness in multi-agent systems (MASs) that is compatible with the internal models and specifications of robotic agents and that enables safe simultaneous operation of collaborating autonomous agents and humans. The goal of SymAware is to provide a comprehensive framework for situational awareness to support sustainable autonomy via agents that actively perceive risks and collaborate with other robots and humans to improve their awareness and understanding, while fulfilling complex and dynamically changing tasks.
Context-aware adaptive visualizations for critical decision making
SYMBIOTIK envisions an effortless interaction dialogue between human and Information Visualization (InfoVis) systems to support decision making processes, inspired by known biological principles and guided by artificial intelligence (AI). Critically, this dialogue requires AI solutions with context awareness, emotion sensing, and expressing capabilities. We propose a novel framework where both the human and the machine cooperate towards a common goal and evolve together. Awareness principles will allow us to engineer complex systems, making them more resilient and more human-centric. We will define an integrative approach for awareness engineering and propose a specific open source implementation. Finally, we will demonstrate and validate the role and added-value of such an awareness framework in two scenarios: supporting novice-to-expert transitions and critical decision making.
Value-Aware Artificial Intelligence
The VALAWAI project will develop a toolbox to build Value-Aware AI resting on two pillars both grounded in science: An architecture for consciousness inspired by the Global Neuronal Workspace model developed on the basis of neurophysiological evidence and psychological data, and a foundational framework for moral decision-making based on psychology, social cognition and social brain science. The project will demonstrate the utility of Value-Aware AI in three application areas for which a moral dimension urgently needs to be included: (i) social media, where we see many negative side effects such as disinformation, polarisation, and the instigation of asocial and immoral behavior, (ii) social robots, which are designed to be helpful or influence human behavior in positive ways but potentially enable manipulation, deceit and harmful behavior, and (iii) medical protocols, where VALAWAI tries to ensure medical decision making is value aligned.