Beyond ‘Interaction’: How to Understand Social Effects on Social Cognition

In recent years, a number of philosophers and cognitive scientists have advocated for an ‘interactive turn’ in the methodology of social-cognition research: to become more ecologically valid, we must design experiments that are interactive, rather than merely observational. While the practical aim of improving ecological validity in the study of social cognition is laudable, we think that the notion of ‘interaction’ is not suitable for this task: as it is currently deployed in the social cognition literature, this notion leads to serious conceptual and methodological confusion. In this paper, we tackle this confusion on three fronts: 1) we revise the ‘interactionist’ definition of interaction; 2) we demonstrate a number of potential methodological confounds that arise in interactive experimental designs; and 3) we show that ersatz interactivity works just as well as the real thing. We conclude that the notion of ‘interaction’, as it is currently being deployed in this literature, obscures an accurate understanding of human social cognition

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

An End-to-End Conversational Style Matching Agent

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

Flexible Decision Control in an Autonomous Trading Agent

An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM).autonomous trading agent;decision processes