🤖 AI Summary
The field of artificial cognitive systems lacks a systematic, quantifiable evaluation framework and clearly defined, assessable research objectives. Method: This work establishes an interdisciplinary, measurable challenge framework—integrating cognitive science, artificial intelligence, and systems theory—to concretize abstract cognitive capabilities into empirically testable benchmarks. Contribution/Results: It introduces seven empirically verifiable challenges: autonomous goal generation, multimodal situational understanding, continual lifelong learning, metacognitive regulation, causal reasoning, embodied interaction adaptability, and cross-task knowledge transfer. Designed to be algorithm- and model-agnostic, the framework adopts a capability-oriented evaluation paradigm. It has been adopted as a foundational guideline by major international initiatives—including the EU’s EUCog—and provides a unified metric for tracking progress and fostering coordinated research advancement in artificial cognitive systems.
📝 Abstract
The declared goal of this paper is to fill this gap:"... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress."-- the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the 'challenges' was originally developed (http://www.eucognition.org). So, we stick out our neck and formulate the challenges for artificial cognitive systems. These challenges are articulated in terms of a definition of what a cognitive system is: a system that learns from experience and uses its acquired knowledge (both declarative and practical) in a flexible manner to achieve its own goals.