The ACORNS project intends to develop, implement and test the mathematical models and computational mechanisms needed to create an artificial agent that is capable of acquiring human verbal communication behaviour based on the memory-prediction theory of intelligence. Unlike conventional pattern recognizers, the ACORNS agent will learn dynamic emergent patterns of speech and non-speech sounds from rich and redundant representations of the acoustic input. Learning will be guided by the intention to fulfil basic needs required to acquire the verbal and non-verbal communication skills of a toddler.
To reach the goal, advances in understanding and realisation are needed in five closely interrelated areas, (1) representations of acoustic signals in multiple parallel temporal and spectral resolutions, (2) methods for patterning signals into coherent structures that correspond to potentially meaningful acoustic events, (3) methods for building and maintaining dynamic emergent patterns in memory, (4) methods for searching in an associative memory, and (5) methods for handling natural interaction between an artificial agent and a human, including verbal and non-verbal (affective) information.
In addition to fundamental scientific knowledge ACORNS will also produce novel techniques that will be integrated and tested into more conventional systems that perform pattern recognition and human-machine interaction. By doing so, ACORNS remedies important weaknesses in today's state-of-the-art speech recognition and dialogue systems.
It is intended that ACORNS will provide a radical new approach to the creation of artificial systems capable of human-like communicative behaviour.