Modeling Social Learning: An Agent-Based Approach

Learning is the process of acquiring or modifying knowledge, behavior, or skills. The ability to learn is inherent to humans, animals, and plants, and even machines are provided with algorithms that could mimic in a restricted way the processes of learning. Humans learn from the time they are born until they die because of a continuous process of interaction between them and their environment. Behavioral Psychology Theories and Social Learning Theories study behavior learned from the environment and social interactions through stimulus-response. Some computer approaches to modeling human behavior attempted to represent the learning and decision-making processes using agent-based models. This dissertation develops a computer model for social learning that allows agents to exhibit behavior learned through social interactions and their environment. The use of an agent-based model allows representing a complex human system in a computer environment. Behavioral Psychology Theories and Social Learning Theories provide the explanatory theoretical framework. The learning processes are implemented using the Rescorla-Wagner Model. The learning structure is implemented using an adaptation of Agent Zero. The decision-making process is implemented using a threshold equation. A use case in youth gang homicides is developed, calibrated, validated, and used for policing and decision making through simulation of multiple case scenarios. The simulation results show the model accuracy in representing learning and decision-making processes similar to those exhibited in the complex human system represented

Escape from the factory of the robot monsters: agents of change

Purpose: The increasing use of robotics within modern factories and workplaces not only sees us becoming more dependent on this technology but it also introduces innovative ways by which humans interact with complex systems. As agent-based systems become more integrated into work environments, the traditional human team becomes more integrated with agent-based automation and, in some cases, autonomous behaviours. This paper discusses these interactions in terms of team composition and how a human-agent collective can share goals via the delegation of authority between human and agent team members. Design/methodology/approach: This paper highlights the increasing integration of robotics in everyday life and examines the nature of how new novel teams may be constructed with the use of intelligent systems and autonomous agents. Findings: Areas of human factors and human-computer interaction are used to discuss the benefits and limitations of human-agent teams. Research limitations/implications: There is little research in (human–robot) (H–R) teamwork, especially from a human factors perspective. Practical implications: Advancing the author’s understanding of the H–R team (and associated intelligent agent systems) will assist in the integration of such systems in everyday practices. Social implications: H–R teams hold a great deal of social and organisational issues that need further exploring. Only through understanding this context can advanced systems be fully realised. Originality/value: This paper is multidisciplinary, drawing on areas of psychology, computer science, robotics and human–computer Interaction. Specific attention is given to an emerging field of autonomous software agents that are growing in use. This paper discusses the uniqueness of the human-agent teaming that results when human and agent members share a common goal within a team

Investigating the cognitive foundations of collaborative musical free improvisation: Experimental case studies using a novel application of the subsumption architecture

This thesis investigates the cognitive foundations of collaborative musical free improvisation. To explore the cognitive underpinnings of the collaborative process, a series of experimental case studies was undertaken in which expert improvisors performed with an artificial agent. The research connects ecological musicology and subsumption robotics, and builds upon insights from empirical psychology pertaining to the attribution of intentionality. A distinguishing characteristic of free improvisation is that no over-arching framework of formal musical conventions defines it, and it cannot be positively identified by sound alone, which poses difficulties for traditional musicology. Current musicological research has begun to focus on the social dimension of music, including improvisation. Ecological psychology, which focuses on the relation of cognition to agent–environment dynamics using the notion of affordances, has been shown to be a promising approach to understanding musical improvisation. This ecological approach to musicology makes it possible to address the subjective and social aspects of improvised music, as opposed to the common treatment of music as objective and neutral. The subjective dimension of musical listening has been highlighted in music cognition studies of cue abstraction, whereby listeners perceive emergent structures while listening to certain forms of music when no structures are identified in advance. These considerations informed the design of the artificial agent, Odessa, used for this study. In contrast to traditional artificial intelligence (AI), which tends to view the world as objective and neutral, behaviour-based robotics historically developed around ideas similar to those of ecological psychology, focused on agent–environment dynamics and the ability to deal with potentially rapidly changing environments. Behaviour-based systems that are designed using the subsumption architecture are robust and flexible in virtue of their modular, decentralised design comprised of simple interactions between simple mechanisms. The competence of such agents is demonstrated on the basis of their interaction with the environment and ability to cope with unknown and dynamic conditions, which suggests the concept of improvisation. This thesis documents a parsimonious subsumption design for an agent that performs musical free improvisation with human co-performers, as well as the experimental studies conducted with this agent. The empirical component examines the human experience of collaborating with the agent and, more generally, the cognitive psychology of collaborative improvisation. The design was ultimately successful, and yielded insights about cognition in collaborative improvisation, in particular, concerning the central relationship between perceived intentionality and affordances. As a novel application of the subsumption architecture, this research contributes to AI/robotics and to research on interactive improvisation systems. It also contributes to music psychology and cognition, as well as improvisation studies, through its empirical grounding of an ecological model of musical interaction

Advances in the Simulation-Based Analysis of Attitude Change

In this paper we provide an overview of the most relevant research work on the simulation of attitudes which evolved in the late 90’s and mainly after the year 2000. The general framework for the modeling, simulation and computational research on attitudes integrates research approaches (both fundamental and applicative) which combine theories from sociology, social psychology, social economics, political science, conflict theories, human-computer interaction areas with complexity theory, computer science, autonomous agents, artificial life, artificial intelligence, machine learning and decision making. One of the main dimensions is that of elaborating agent-based studies and simulations of the attitude dynamics

The simulation of social exchange: developing a multidimensional model of exchange rules in human interaction.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Social exchange underpins social structure and as such, social exchange theory has taken a central role in the field of social psychology. The study of exchange rules and how they interact with each other is an area within this theory that has not received much attention up until now. This study has aimed to study the exchange rules of fairness, reciprocity, self-interest, vicinity and ingroup favouritism within an interacting exchange network. Agent based computational modelling with a comparison to empirical data has been proposed as a novel method to uncover the process of exchange from the bottom up. The results of the study indicate that there exists no universal combination of exchange rules that can predict human behaviour in all settings. Exchange rules are adopted based on institutional norms as well as norms that emerge during interaction

Planning and sequential decision making for human-aware robots

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis explores the use of probabilistic techniques for enhancing the interaction between a human and a robotic assistant. The human in this context is regarded as an integral part of the system, providing a major contribution to the decision making process and is able to overwrite, re-evaluate and correct decisions made by the robot to fulfil her or his true intentions and ultimate goals and needs. Conversely, the robot is expected to behave as an intelligent collaborative agent that predicts human intentions and makes decisions by merging learned behaviours with the information it cmTently possesses. The work is motivated by the rapid increase of the application domains in which robotic systems operate, and the presence of humans in many of these domains. The proposed framework facilitates human-robot social integration by increasing the synergy between robot's capabilities and human needs, primarily during assistive navigational tasks. The first part of the thesis ets the groundwork by developing a path-planning/re-planning strategy able to produce smooth feasible paths to address the issue of navigating a robotic wheelchair in cluttered indoor environments. This strategy integrates a global path-planner that operates as a mission controller, and a local reactive planner that navigates locally in an optimal manner while preventing collisions with static and dynamic obstacles in the local area. The proposed strategy also encapsulates social behaviour, such as navigating through preferred routes, in order to generate socially and behavioura11y acceptable plans. The work then focuses on predicting and responding to human interactions with a robotic agent by exploiting probabilistic techniques for sequential decision making and planning under uncertainty. Dynamic Bayesian networks and partially observable Markov decision processes are examined for estimating human intention in order to minimise the flow of information between the human and the robot during navigation tasks. A framework to capture human behaviour, motivated by the human action cycle as derived from the psychology domain is developed. This framework embeds a human-robot interaction layer, which defines variables and procedures to model interaction scenarios, and facilitates the transfer of information during human-robot collaborative tasks. Experiments using a human-operated robotic wheelchair carrying out navigational daily routines are conducted to demonstrate the capacity of the proposed methodology to understand human intentions and comply with their long term plans. The results obtained are presented as the outcome of a set of trials conducted with actor users, or simulated experiments based on real scenarios

Designing friends

Embodied Conversational Agents are virtual humans that can interact with humans using verbal and non-verbal forms of communication. In most cases, they have been designed for short interactions. This paper asks the question how one would start to design synthetic characters that can become your friends. We look at insights from social psychology and propose a methodology for designing friends

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe