2013/05/09 by David L. Dowe, Dowe, David L., José Hernández‐Orallo +2
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1305.1991
arxiv created 2013/05/09 · openalex publication_date 2013/05/09 · arxiv updated 2013/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The analysis of the adaptive behaviour of many different kinds of systems such as humans, animals and machines, requires more general ways of assessing their cognitive abilities. This need is strengthened by increasingly more tasks being analysed for and completed by a wider diversity of systems, including swarms and hybrids. The notion of universal test has recently emerged in the context of machine intelligence evaluation as a way to define and use the same cognitive test for a variety of systems, using some principled tasks and adapting the interface to each particular subject. However, how far can universal tests be taken? This paper analyses this question in terms of subjects, environments, space-time resolution, rewards and interfaces. This leads to a number of findings, insights and caveats, according to several levels where universal tests may be progressively more difficult to conceive, implement and administer. One of the most significant contributions is given by the realisation that more universal tests are defined as maximisations of less universal tests for a variety of configurations. This means that universal tests must be necessarily adaptive.