用‘认知形态’模型模拟智能体如何像人一样高效处理复杂现实。
Shapes of Cognition for Computational Cognitive Modeling
- 基于感知、语言、记忆等知识的‘认知形态’框架，模拟人类认知模式。
- 通过典型性预期与习惯化行为，显著降低认知负荷，提升决策效率。
- 适用于可信、可解释的智能系统构建，尤其适合关键领域应用。
认知形态是一种面向语言赋能智能体（LEIAs）的新型计算认知建模范式。认知形态是记忆中的感官、语言、概念、情景和程序性知识的组合结构，使智能体能像人类一样应对现实复杂性：通过预期典型性、识别模式、习惯化行动、类比推理、满足即可，从而在情境允许范围内最大限度降低认知负担。对于非典型结果，则采用基于形态的恢复机制，如即时学习、向人类伙伴求助，或寻求虽不完美但可操作的情境理解。尽管‘认知形态’是统称，却具备明确内涵：其建模涉及具体目标、假设、策略、知识库及实际模型，均在特定认知架构中实现。这种精确性既用于验证假设，也服务于构建可信、可扩展且值得信赖的实用智能系统，即便在关键领域亦然。尽管以LEIA为例，其原则仍可广泛适用，为知识驱动与混合人工智能注入新活力。
原文摘要 · Abstract (English)
Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language-Endowed Intelligent Agents (LEIAs). Shapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree situations permit. Atypical outcomes are treated using shapes-based recovery methods, such as learning on the fly, asking a human partner for help, or seeking an actionable, even if imperfect, situational understanding. Although shapes is an umbrella term, it is not vague: shapes-based modeling involves particular objectives, hypotheses, modeling strategies, knowledge bases, and actual models of wide-ranging phenomena, all implemented within a particular cognitive architecture. Such specificity is needed both to vet our hypotheses and to achieve our practical aims of building useful agent systems that are explainable, extensible, and worthy of our trust, even in critical domains. However, although the LEIA example of shapes-based modeling is specific, the principles can be applied more broadly, giving new life to knowledge-based and hybrid AI.
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