为对话型AI设计全新动机架构，让机器更懂人心。
A Motivational Architecture for Conversational AGI
- 将生理需求动机重构为对话中的能力、归属、美感等心理调节
- 提出十阶段处理流程与快慢决策双策略，提升回应精准度
- 适合构建有情感智能的聊天助手或通用智能体
认知人工智能中的动机架构传统上针对物理代理调节身体需求。对话型代理则处于不同环境：其感知-运动回路是语言，环境是用户不断变化的心理状态，行动包括言语、工具调用和策略性沉默。本文提出对OpenPsi动机体系的对话重解，并结合MetaMo的高层动机框架，应用于模块化执行基础的代理。将稳态机制重新定义为对话原生的调节目标：能力、不确定性降低、归属感、亲密度、合法性、关怀感与审美一致性，而非身体缺陷。本文提出三项贡献：一个十阶段动机处理流水线，将认知调制与情境评估分离；一种融合紧迫驱动快速响应与多目标优化的双重决策策略；以及预行动感受与后行动情绪在功能上的区分。该框架被应用于两个示例代理——CompanionAgent与ResearchAgent，也初步拓展至社交机器人与通用人类级AGI。
原文摘要 · Abstract (English)
Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs. Conversational agents operate in a different regime: their sensorimotor loop is linguistic, their environment is a user's evolving mental state, and their consequential actions are speech acts, tool invocations, and strategic silences. This paper proposes a conversational reinterpretation of the OpenPsi motivational lineage, coupled to MetaMo's higher-level motivational scaffold, for agents built on a modular execution substrate. Homeostasis is recast in dialogue-native terms: the agent regulates competence, uncertainty reduction, affiliation, affinity, legitimacy, nurturing, and aesthetic coherence rather than bodily deficits. We propose three contributions: a ten-stage motivational processing pipeline that architecturally separates cognitive modulation from situational appraisal; a dual decision strategy blending urgency-driven fast response with deliberative multi-goal optimization; and an architecturally useful distinction between pre-action feelings and post-action emotions as functionally different forms of affect. We specialize the framework to two example agents -- CompanionAgent and ResearchAgent -- and sketch its extension to social robotics and domain-generic human-level AGI.
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