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Dynamic Interactive Artificial Intelligence: Sketches for a Future AI Based on Human-Machine Interaction
. isal2020, ALIFE 2020: The 2020 Conference on Artificial Life139-145, (July 13–18, 2020) doi: 10.1162/isal_a_00350
We propose to designate as dynamic interactive artificial intelligence (dAI) a cross-section of existing work in artificially designed and artificially evolved systems meant for minimal forms of interaction with human users. This approach borrows principles from artificial life and human movement science to avoid pitfalls of traditional AI. Counter to tradition, it prioritizes user-machine inter-dependence over autonomy. It starts small and relies on incremental growth instead of trying to implement advanced complete functionality. It assumes a perceptual ontology founded on movement coordination rather than object classification. Its development process is better described as reverse self-organization rather than reverse engineering. dAI can be viewed as a precursor to or pre-condition for enactive AI and an alternative to traditional frameworks grounded on information representation. We then give examples from our work in human movement science where we have used minimal dynamic interactive agents to induce specific beneficial effects in human participants’ movement skills. We also show how dAI can be exploited by both connectionist and symbolic AI.
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life109-110, (July 23–27, 2018) doi: 10.1162/isal_a_00028
Dexterous assistive devices constitute one of the frontiers for hybrid human-machine systems. Manipulating unstable systems requires task-specific anticipatory dynamics. Learning this dynamics is more difficult when tasks, such as carrying liquid or riding a horse, produce unpredictable, irregular patterns of feedback and have hidden dimensions not projected as sensory feedback. We addressed the issue of coordination with complex systems producing irregular behaviour, with the assumption that mutual coordination allows for non-periodic processes to synchronize and in doing so to become regular. Chaos control gives formal expression to this: chaos can be stabilized onto periodic trajectories provided that the structure of the driving input takes into account the causal structure of the controlled system. Can we learn chaos control in a sensorimotor task? Three groups practiced an auditory-motor synchronization task by matching their continuously sonified hand movements to sonified tutors: a sinusoid served as a Non-Interactive Predictable tutor (NI-P), a chaotic system stood for a Non-Interactive Unpredictable tutor (NI-U), and the same system weakly driven by the participant’s movement stood for an Interactive Unpredictable tutor (I-U). We found that synchronization, dynamic similarity, and causal interaction increased with practice in I-U. Our findings have implications for current efforts to find more adequate ways of controlling complex adaptive systems.