Agent-based social simulation (with multi-agent systems), which is an important aspect of social computing, can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents and therefore their social interactions. A cognitive architecture is a domain-generic computational cognitive model that may be used for a broad multiple-domain analysis of individual behavior. In this article, an example of a cognitive architecture is given, and its applications to social simulation described. Some challenging issues in this regard are outlined.

Abstract. As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application to social simulation will be sketched. 1 Defining Cognitive Architectures As we know, a cognitive architecture is a broadly-scoped, domain-generic computational cognitive model, capturing essential structures and processes of the mind, to be used for a broad, multiple-level, multiple-domain analysis of cognition and behavior (Newell 1990, Sun 2002). The architecture for a building consists of its overall framework and its overall design, as well as roofs, foundations, walls, windows, floors, and so on. Furniture

This full-day tutorial introduces participants to CLARION, a dual-process/dual-representation cognitive architecture that focuses on the distinction between explicit and implicit cognitive processes. CLARION is also integrative, involving cognition, motivation, metacognition, and so on. This tutorial presents a detailed description, along with many simulations, advanced topics, and formal results. Although some prior exposure to cognitive architectures and artificial neural networks can be helpful, prior understanding of these areas is not required, as the full-day format allows a detailed presentation of basic, as well as advanced, topics related to cognitive modeling using CLARION. This tutorial will enable participants to apply the basic concepts, theories, and computational models of CLARION to their own work. Overview CLARION is a cognitive architecture composed of four main subsystems: the Action-Centered Subsystem (ACS), the Non-Action-Centered Subsystem (NACS), the Meta-Cognitive Subsystem (MCS), and the Motivational Subsystem (MS). The ACS is used mainly for action decision-making. The NACS is usually a slave system to the ACS and is used to store declarative and episodic knowledge. This subsystem is also responsible for reasoning in CLARION. The MS is responsible for determining motivational drive levels (which in turn lead to the setting of goals). The MCS is responsible for cognitive monitoring and parameter setting in both the ACS and NACS, and makes the goal setting determinations based on drive levels reported from the MS. In addition to the aforementioned subsystem structure, CLARION is based on two other basic assumptions: representational differences and learning differences of two different types of knowledge: implicit versus explicit (Sun,