Towards a Theory of Awareness at ICAART 2024

At the ICAART 2024 Conference in Rome we had a Special Session organized by Luc Steels on the Awareness Inside topic funded by the EIC Pathfinder programme.

In the session I presented the initial developments towards a theory of awareness that the ASLab team is doing inside the CORESENSE and METATOOL projects.

We did a short, ten minute presentation, and after that we had an interesting conversation around the ideas presented and the possibility of developing a complete model-based theory of aware agents.

The presentation addressed the possibility of having a solid theory of awareness both for humans and machines and what are the elements that such a theory should have. We talked about two of these elements: the clear delimitation of a domain of phenomena, and the essential concepts that are the cornerstones of the theory.

These were the concepts that we proposed/discussed:

  • Sensing: The production of information for the subject from an object.
  • Perceiving: The integration of the sensory information bound to an object into a model of the object.
  • Model: Integrated actionable representation; an information structure that sustains a modelling relation.
  • Engine: Set of operations over a model.
  • Inference: Derive conclusions from the model. Apply engines to model.
  • Valid inference: A inference whose result matches the phenomenon at the modelled object.
  • Exert a model: Perform valid inferences from the model.
  • Understanding: Achieving exertability of a model of the object/environment.
  • Specific understanding: Understanding concerning a specific set of exertions.
  • Mission understanding: Understanding concerning a set of mission-bound exertions.
  • Omega understanding: Understanding all possible exertions of a model in relation to an object.
  • Awareness: Real-time understanding of sensory flows.
  • Self-awareness: Subject-bound awareness. Awareness concerning inner perception.

Get the complete presentation:

And the paper:

AI for Conscious Machines

November 14, 2013

The many challenges of Artificial Intelligence. November 13-15 2023

A workshop to evaluate the challenges and repercussions of the “new” Artificial Intelligence (AI) taking place at Residencia La Cristalera, Miraflores de la Sierra (Madrid).

It has been organized by Juan Poyatos (CNB), Juan Rodríguez (IIIA), and David Ríos (ICMAT) abd sponsored by AIHub-CSIC and the AXA Research Fund.

There I gave a talk on machine consciousness based on our current activity in the Horizon Europe projects CORESENSE and METATOOL.

Asking ChatGPT about our possibilitites in CORESENSE

Much in the vein of ancient times people asking Pythia about their future, I asked ChatGPT about our possibilities in the CORESENSE project to create machines capable of understanding their environment. This is what it said:

Me: Can a machine understand its environment?

ChatGPT: Whether a machine can understand its environment depends on its capabilities and design. Some machines, such as certain types of robots or artificial intelligence systems, are designed to gather and process information about their environment in order to make decisions and take actions. These machines may be able to build a model or representation of their environment and use this information to navigate and interact with it. However, the extent to which these machines can truly “understand” their environment is a complex and contentious topic that is not fully understood. Some argue that machines can only simulate understanding and do not have the same level of conscious awareness and comprehension as humans. Others believe that machines can achieve true understanding through advanced algorithms and learning systems. Ultimately, the ability of a machine to understand its environment depends on its design and capabilities.

ChatGPT is an AI-based chatbot developed by OpenAI to have a dialogue much a la Turing test. The program is a very large English language model that has been fine-tuned with both supervised and reinforcement learning techniques.

A new Horizon Europe project: CoreSense

We have signed a grant agreement with the EC to coordinate a new research project on intelligent robotics. The CoreSense project will develop a new hybrid cognitive architecture to make robots capable of understanding and being aware of what is going on. The project will start on October 1, 2022 will span four years (2022-2026) and joins six partners across europe in an effort to push forward the limits of robotic cognition.

Cognitive robots are augmenting their autonomy, enabling them to deployments in increasingly open-ended environments. This offers enormous possibilities for improvements in human economy and wellbeing. However, it also poses strong risks that are difficult to assess and control by humans. The trend towards increased autonomy conveys augmented problems concerning reliability, resilience, and trust for autonomous robots in open worlds. The essence of the problem can be traced to robots suffering from a lack of understanding of what is going on and a lack of awareness of their role in these situations. This is a problem that artificial intelligence approaches based on machine learning are not addressing well. Autonomous robots do not fully understand their open environments, their complex missions, their intricate realizations, and the unexpected events that affect their performance. An improvement in the capability to understand of autonomous robots is needed.

The CoreSense project tries to provide a solution to this need in the form of a AI theory of understanding, a theory of robot awareness, some enginering-grade reusable software assets to apply these theories in real robots. The project will build three demonstrations of its capability to augment resilience of drone teams, augment flexibility of manufacturing robots, and augment human alignment of social robots.

In summary, CoreSense will develop a cognitive architecture for autonomous robots based on a formal concept of understanding, supporting value-oriented situation understanding and self-awareness to improve robot flexibility, resilience and explainability.

There are six project partners:

Universidad Politécnica de Madrid – ES – Coordinator
Delft University of Technology – NL
Fraunhofer IPA – DE
Universidad Rey Juan Carlos – ES
PAL Robotics – ES
Irish Manufacturing Research – IR

Principal Investigator: Ricardo Sanz


Our dissemination team has prepared another movie describing some robotics activities inside the ROBOMINERS Horizon 2020 project. In the movie we see activity at UPM (Spain), TalTech (Estonia), and TAU (Finland) in some of the themes related to ROBOMINERS.

We at UPM are involed in two main activities: the implementation of the RM2 robot miner pratotype and the construction of the high-level autonomy software.

Architectures of Mind

The investigation about what are the best architectures for mind construction has too many intervening threads and interferences. Sometimes heterogeneous people of different domains get together to try to clarify some of the issues concerning mind architecture. In many of these gatherings, people from science and technology try to devise strategies for building computational models or system archures to create artificial minds.

Proposals on mind engineering processes.

One of these efforts was the EU Funded ICEA Project. The IST 027819 ICEA Integrating Cognition, Emotion and Autonomy was a four-year project, funded by IST Cognitive Systems Unit. The Project was focused on brain-inspired cognitive architectures, robotics and embodied cognition, bringing together cognitive scientists, neuroscientists, psychologists, computational modelers, roboticists and control engineers. The primary aim of the project is to develop a novel cognitive systems architecture integrating cognitive, emotional and bioregulatory (self-maintenance) processes, based on the architecture and physiology of the mammalian brain.

The works but extremely interesting and the teams involved were mostly very active and dedicated to the work. However, the results obtained -basically more elaborated scientific ideas- didn’t have a translation in a concrete architectural implementation of a system that could prove the insights. Different systems wer implemented -real, simulated- but the overall picture was a bit lost.

We need stronger tema integration and specially more convergent objectives to get these types of activities produce the expected and potential results. This is not an easy taks however, as the goal of a cognitive psychologist of an industrial control engineer seem so far a part that convergence sounds more lie a myth than a real, strategical possibility.

All this said, something in fully basic: technology departs from science. Mind engineering must depart from mind since and hence, convergence is necessary. This will only happen if we broaden the tagets and pursue a General Theory of Mind not trapped in the details of rat brains or humanoid robot computer-laden hearths.