Construction of Meanings in Biological and Artificial Agents

In this chapter, we focus on the issue of understanding in various types of agents. Our main goal is to build up notions of meanings and understanding in neutral and non-anthropocentric terms that would not exclude preverbal living organisms and artificial systems by definition. By analyzing the evolutionary context of understanding in living organisms and the representation of meanings in several artificially built systems, we come to design principles for building “understanding” artificial agents and formulate necessary conditions for the presence of inherent meanings. Such meanings should be based on interactional couplings between the agents and their environment, and should help the agents to orient themselves in the environment and to satisfy their goals. We explore mechanisms of action-based meaning construction, horizontal coordination, and vertical transmission of meanings and exemplify them with computational models.

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

Agents and Social Interaction

Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area, as in the field of human computer interaction, generally, is becoming more interdisciplinary in nature. In this spirit, the present chapter will survey concepts and theory from social psychology, a field many researchers may be unfamiliar with. Dennett’s notion of the intentional system will provide some initial grounding for the notion of social interaction, along with a brief discussion of conversational agents. The body of the chapter will then survey the areas of animal behavior and social psychology most relevant to human-agent interaction, concentrating on the areas of interpersonal relations and social perception. Within the area of social perception, the focus will be on the topics of emotion and attribution theory. Where relevant, research in the area of agent-human interaction will be discussed. The chapter will conclude with a brief survey of the use of agent-based modeling and simulation in social theory. The future looks very promising for researchers in this area; the complex problems involved in developing artificial agents who have mind-like attributes will require an interdisciplinary effort.

The Science of Social Emergence

Sociology should be the foundational science of social emergence. But to date, sociologists have neglected emergence, and studies of emergence are more common within microeconomics. Moving forward, I argue that a science of social emergence requires two advances beyond current approaches—and that sociology is better positioned than economics to make these advances. First, consistent with existing critiques of microeconomics, I argue that we need a more sophisticated representation of individual agents. Second, I argue that multi-agent models need a more sophisticated representation of interaction processes. The agent communication languages currently used by multi-agent systems researchers are not appropriate for modeling human societies. I conclude by arguing that the scientific study of interaction and emergence will have to migrate out of microeconomics and become a part of sociology. Sociologists, for their part, should embrace multi-agent modeling to pursue a more rigorous study of these traditional sociological issues.

Developing Relationships Between Autonomous Agents

This chapter discusses certain issues in the development of Virtual Learning Environments (VLEs) populated by autonomous social agents, with specific reference to existing applications designed to promote pro-social behaviour among children. We begin by describing the ways in which human groups are organised and maintained, and present the primary school class as a particular example of a social network. Contemporary psychological descriptions of bullying are explained, and current anti-bullying interventions are briefly reviewed. Two VLEs are described, which have been designed to counteract the problems inherent in bullying as exemplars of social and educational environments. This chapter concludes in Part II where the requirements for believable, autonomous agents, used in virtual learning environments, are outlined.

Emergent Reasoning Structures in Law

In modern legal systems, a large number of autonomous agents can achieve reasonably fair and accurate decisions in tens of thousands of legal cases. In many of those cases, the issues are complicated, the evidence is extensive, and the reasoning is complex. The decision-making process also integrates legal rules and policies with expert and non-expert evidence. This chapter discusses two major types of reasoning that have emerged to help bring about this remarkable social achievement: systems of rule-based deductions and patterns of evidence evaluation. In addition to those emergent structures, second-order reasoning about legal reasoning itself not only coordinates the decision-making, but also promotes the emergence of new reasoning structures. The chapter analyzes these types of reasoning structures using a many-valued, predicate, default logic – the Default-Logic (D-L) Framework. This framework is able to represent legal knowledge and reasoning in actual cases, to integrate and help evaluate expert and non-expert evidence, to coordinate agents working on different legal problems, and to guide the evolution of the knowledge model over time. The D-L Framework is also useful in automating portions of legal reasoning, as evidenced by the Legal Apprenticetm software. The framework therefore facilitates the interaction of human and non-human agents in legal decision- making, and makes it possible for non-human agents to participate in the evolution of legal reasoning in the future. Finally, because the D-L Framework itself is grounded in logic and not on theories peculiar to the legal domain, it is applicable to other knowledge domains that have a complexity similar to that of law and solve problems through default reasoning.

Reputation

This chapter is focused on social reputation as a fundamental mechanism in the diffusion and possibly evolution of socially desirable behaviour (e.g., cooperation, altruism, and norm-abiding behaviour). Reputation is seen as both a property of agents and a process of transmission of beliefs about this property. The main current views and hypotheses about reputation are found to underestimate the importance of the process of transmission. Next, a cognitive analysis of reputation and of its transmission is presented. Hypotheses concerning the transmissibility of reputation are discussed, and checked by means of simulation. Finally, speculations concerning the role of reputation in the evolution of reciprocal altruism are discussed, and ideas for future studies are sketched out.

Developing Relationships Between Autonomous Agents

This chapter is a continuation from Part I, which has described contemporary psychological descriptions of bullying in primary schools and two Virtual Learning Environments (VLEs) designed as anti-bullying interventions. The necessary requirements for believable, autonomous agents used in virtual learning environments are now outlined. In particular, we will describe the technical and engagement-oriented considerations that need to be made. The chapter concludes with recommendations of how to meet these needs and how to design a VLE by including potential users in the development process.

Mapping Hybrid Agencies Through Multiagent Systems

In this chapter, we give an overview of the results of a Human-Robot Interaction experiment, in a near zerocontext environment. We stimulate the formation of a network joining together human agents and non-human agents, in order to examine emergent conditions and social actions. Human subjects, in teams of three to four, are presented with a task–to coax a robot (by any means) from one side of a table to the other–not knowing with what sensory and motor abilities the robotic structure is equipped. On the one hand, the “goal” of the exercise is to “move” the robot through any linguistic or paralinguistic means. But, from the perspective of the investigators, the goal is both broader and more nebulous–to stimulate any emergent interactions whatsoever between agents, human or non-human. Here we discuss emergent social phenomena in this assemblage of human and machine, in particular, turn-taking and discourse, suggesting (counter-intuitively) that the “transparency” of non-human agents may not be the most effective way to generate multi-agent sociality.

Hybrid Emotionally Aware Mediated Multiagency

Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial intelligence aims at constructing agents that can emulate thinking processes, but artificial life still lacks emotions and all the consequences that come from them. This work introduces an emotionally aware framework geared towards multi-agent societies. Basing our model on the shoulders of solid foundations created by pioneers who first explored the coupling of emotions and agency, we extend their ideas to include inter-agent interaction and virtual genetics as key components of an agent’s emotive state. We also introduce possible future applications of this framework in consumer products as well as research endeavors.