Towards Social Identity in Socio-Cognitive Agents

Current architectures for social agents are designed around some specific units of social behaviour that address particular challenges. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social reality as an interpretation of the physical world with social meanings and selective deployment of cognitive resources adequate to the situation. We identify several design principles that should be considered while designing agent architectures for socio-cognitive systems. Taking these remarks into account, we propose a socio-cognitive agent model based on the concept of Cognitive Social Frames that allow the adaptation of an agent's cognition based on its interpretation of its surroundings, its Social Context. Our approach supports an agent's reasoning about other social actors and its relationship with them. Cognitive Social Frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of Social Identity

Situating Cognition within the Virtual World

Cognitive architectures and virtual environments have a long history of use within the cognitive science community. Few studies, however, have sought to combine the use of these technologies to support computational studies into embodied, extended, situated and distributed (EESD) cognition. Here, we explore the extent to which the ACT-R cognitive architecture and the Unity game engine can be used for these purposes. A range of issues are discussed including the respective responsibilities that the cognitive architecture and game engine have for the implementation of specific processes, the extent to which the representational and computational capabilities of cognitive architectures are suited to the modeling of EESD cognitive systems, and the extent to which the kind of embodiment seen in the case of so-called ‘embodied virtual agents’ resembles that seen in the case of real-world bio-cognitive systems. These issues are likely to inform the focus of future research efforts concerning the integrative use of virtual environments and cognitive architectures for the computational modeling and simulation of EESD cognitive processes

Situating Cognition within the Virtual World

Cognitive architectures and virtual environments have a long history of use within the cognitive science community. Few studies, however, have sought to combine the use of these technologies to support computational studies into embodied, extended, situated and distributed (EESD) cognition. Here, we explore the extent to which the ACT-R cognitive architecture and the Unity game engine can be used for these purposes. A range of issues are discussed including the respective responsibilities that the cognitive architecture and game engine have for the implementation of specific processes, the extent to which the representational and computational capabilities of cognitive architectures are suited to the modeling of EESD cognitive systems, and the extent to which the kind of embodiment seen in the case of so-called ‘embodied virtual agents’ resembles that seen in the case of real-world bio-cognitive systems. These issues are likely to inform the focus of future research efforts concerning the integrative use of virtual environments and cognitive architectures for the computational modeling and simulation of EESD cognitive processes

Individual behavior and macro social properties. An agent based model

The paper aims at presenting an agent-based modeling exercise to illustrate how small differences in the cognitive properties of agents can generate very different macro social properties. We argue that it is not necessary to assume highly complicated cognitive architectures to introduce cognitive properties that matter for computational social science purposes. Our model is based on different simulation settings characterized by a gradual sophistication of behavior of agents, from simple heuristics to macro-micro feedback and other second-order properties. Agents are localized in a spatial interaction context. They have an individual task but are influenced by a collective coordination problem. The simulation results show that agents can generate efficiency at a macro level particularly when socio-cognitive sophistication of their behavior increase

Cognitive Architectures for Language Agents

Recent efforts have incorporated large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning. However, these efforts have largely been piecemeal, lacking a systematic framework for constructing a fully-fledged language agent. To address this challenge, we draw on the rich history of agent design in symbolic artificial intelligence to develop a blueprint for a new wave of cognitive language agents. We first show that LLMs have many of the same properties as production systems, and recent efforts to improve their grounding or reasoning mirror the development of cognitive architectures built around production systems. We then propose Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematize diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework. Finally, we use the CoALA framework to highlight gaps and propose actionable directions toward more capable language agents in the future.Comment: 16 pages of main content, 10 pages of references, 5 figures. Equal contribution among the first two authors, order decided by coin flip. A CoALA-based repo of recent work on language agents: https://github.com/ysymyth/awesome-language-agent

Assessing and characterizing the cognitive power of machine consciousness implementations

Proceeding of: AAAI 2009 Biologically Inspired Cognitive Architectures-II (BICA-2009). Technical Report FS-09-01. Washington, D.C. EE.UU, 5-7 Noviembre 2009.Many aspects can be taken into account in order to assess the power and potential of a cognitive architecture. In this paper we argue that ConsScale, a cognitive scale inspired on the development of consciousness, can be used to characterize and evaluate cognitive architectures from the point of view of the effective integration of their cognitive functionalities. Additionally, a graphical characterization of the cognitive power of artificial agents is proposed as a helpful tool for the analysis and comparison of Machine Consciousness implementations. This is illustrated with the application of the scale to a particular problem domain in the context of video game synthetic bots.This research has been supported by the Spanish Ministry of Education under CICYT grant TRA2007-67374-C02-02.Publicad

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges