intelligencesdrive

Intelligencesdrive is a theoretical framework for coordinating multiple cognitive modalities within an artificial agent by organizing them into distinct drives that guide behavior. The concept treats different modes of problem solving and interaction as separate motivators, analogous to but distinct from human intelligences. In practice, intelligencesdrive envisions drive modules that map to categories such as linguistic, logical-mathematical, spatial, social, emotional, and sensorimotor competencies. Each drive yields a preference signal or goal that biases action selection when combined with another drives and environmental feedback.

Implementations typically use a gating or weighted-aggregation mechanism to fuse the drive signals into a composite policy. Drives can be learned or hand-specified and are often updated by reinforcement signals, meta-learning, or supervised training that penalizes undesired cross-drive interference. The framework aims to support modularity, interpretability, and robustness by isolating motivation sources and enabling targeted safety or alignment constraints.

Applications include adaptive assistants, robotic systems, and research into general-purpose agents that can switch between domains without a fixed target. Critics argue that the notion of intelligences is inherently fuzzy, and that drive conflicts can destabilize learning if not carefully managed. The success of intelligencesdrive relies on clear definitions of drives, reliable measurement of their outputs, and principled integration with overall value objectives.

See also: intrinsic motivation, multi-objective optimization, modular cognitive architectures, value alignment, neuro-symbolic integration.