Modelling Socially Intelligent Agents

The perspective of modelling agents rather than using them for a specificed purpose entails a difference in approach. In particular an emphasis on veracity as opposed to efficiency. An approach using evolving populations of mental models is described that goes some way to meet these concerns. It is then argued that social intelligence is not merely intelligence plus interaction but should allow for individual relationships to develop between agents. This means that, at least, agents must be able to distinguish, identify, model and address other agents, either individually or in groups. In other words that purely homogeneous interaction is insufficient. Two example models are described that illustrate these concerns, the second in detail where agents act and communicate socially, where this is determined by the evolution of their mental models. Finally some problems that arise in the interpretation of such simulations is discussed

Pragmatic Holism

The reductionist/holist debate seems an impoverished one, with many participants appearing to adopt a position first and constructing rationalisations second. Here I propose an intermediate position of pragmatic holism, that irrespective of whether all natural systems are theoretically reducible, for many systems it is completely impractical to attempt such a reduction, also that regardless if whether irreducible `wholes' exist, it is vain to try and prove this in absolute terms. This position thus illuminates the debate along new pragmatic lines, and refocusses attention on the underlying heuristics of learning about the natural world

Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity

Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has allowed the emergence of heterogeneity

Artificial Science – a simulation test-bed for studying the social processes of science

it is likely that there are many different social processes occurring in different parts of science and at different times, and that these processes will impact upon the nature, quality and quantity of the knowledge that is produced in a multitude of ways and to different extents. It seems clear to me that sometimes the social processes act to increase the reliability of knowledge (such as when there is a tradition of independently reproducing experiments) but sometimes does the opposite (when a closed clique act to perpetuate itself by reducing opportunity for criticism). Simulation can perform a valuable role here by providing and refining possible linkages between the kinds of social processes and its results in terms of knowledge. Earlier simulations of this sort include Gilbert et al. in [10]. The simulation described herein aims to progress this work with a more structural and descriptive approach, that relates what is done by individuals and journals and what collectively results in terms of the overall process

Meta-Genetic Programming: Co-evolving the Operators of Variation

The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed

Learning Appropriate Contexts

Genetic Programming is extended so that the solutions being evolved do so in the context of local domains within the total problem domain. This produces a situation where different “species” of solution develop to exploit different “niches” of the problem – indicating exploitable solutions. It is argued that for context to be fully learnable a further step of abstraction is necessary. Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify when it is the content of a model is wrong and when it is the context. Some principles of learning to identify useful contexts are proposed

The Possible Incommensurability of Utilities and the Learning of Goals

This is a short article to examine the following possibility: that a single agent might simultaneously have different utilities that are incommensurable. Some arguments aginst this possiblity are considered and rejected. Two practical examples are given and its implications in terms of goal change and learning are discussed

Social Embeddedness and Agent Development

Two different reasons for using agents are distinguished: the `engineering' perspective and the `social simulation' perspective. It is argued that this entails some differences in approach. In particular the former will want to prevent unpredictable emergent features of their agent populations whilst the later will want to use simulation to study precisely this phenomena. A concept of `social embeddedness' is explicated which neatly distinguishes the two approaches. It is argued that such embedding in a society is an essential feature of being a truly social agent. This has the consequence that such agents will not sit well within an `engineering' methodology

Using Localised ‘Gossip’ to Structure Distributed Learning

The idea of a “memetic” spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlappingt localities in a space and solutions are then evolved in those localites. Good solutions are not only crossed with others to search for better solutions but also they propogate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occcurence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process occurs can effect its efficiency