arvoiot

Arvoiot is a neologism used in theoretical discussions of value-based computation to denote a class of modular digital artifacts. An arvoiot is conceived as an autonomous agent capable of reasoning about multiple objectives within uncertain environments and selecting actions that balance competing goals. The term is not standardized and may be used differently in different texts.

A typical arvoiot comprises a value assessment subsystem that assigns scores to outcomes, a context model that represents environmental state and agent capabilities, a decision engine that combines scores under constraints, and an interaction interface to perform actions and receive feedback. Some proposals include a learning component that adapts value weights over time.

In thought experiments, arvoiots are used to explore questions of value alignment and decision-making under ambiguity. In simulations and games, they serve as adaptive non-player characters that demonstrate multi-objective tradeoffs. In AI ethics discussions, they illustrate how values may be specified, tested, and revised.

The word arvoiot appears in multiple independent writings since the early 21st century, but there is no unified definition or origin story. The term is often contrasted with generic autonomous agents or value-oriented optimization systems, highlighting its emphasis on modular value reasoning rather than fixed rules.

The lack of a single definition can hinder communication. Practical challenges include ensuring interpretability, preventing value drift, and avoiding misuse in overconfident optimization. Proponents argue that arvoiot-like architectures help frame discussions about aligning artificial agents with human values.

See also autonomous agent, multi-objective optimization, value alignment, ethical AI.