Understanding Autonomous Interaction

. Autonomy is a necessary part of the design of agents flexible enough to function effectively and efficiently in a sophisticated world. Much work, however, has taken a very restricted view of what is entailed by autonomous interaction; in particular, the effects of an interaction have, to some extent, been guaranteed. In this paper, we argue that no facet of interaction can ever be guaranteed, and that if agents are to be autonomous, they must be able to cope with this inherent uncertainty. We propose a model of autonomous interaction in response, which addresses these concerns, and which can be viewed as a process of motivated discovery. This approach has two important aspects: first, modelling the motivations of the agent allows a more adequate model of autonomy to be achieved, and also provides a control strategy for the process of interaction; second, the discovery paradigm provides a suitable framework for effective action and reasoning in an uncertain environment. 1 Introduction..

Learning and Co-operation in Mobile Multi-Robot Systems

Merged with duplicate record 10026.1/1984 on 27.02.2017 by CS (TIS)This thesis addresses the problem of setting the balance between exploration and exploitation in teams of learning robots who exchange information. Specifically it looks at groups of robots whose tasks include moving between salient points in the environment. To deal with unknown and dynamic environments,such robots need to be able to discover and learn the routes between these points themselves. A natural extension of this scenario is to allow the robots to exchange learned routes so that only one robot needs to learn a route for the whole team to use that route. One contribution of this thesis is to identify a dilemma created by this extension: that once one robot has learned a route between two points, all other robots will follow that route without looking for shorter versions. This trade-off will be labeled the Distributed Exploration vs. Exploitation Dilemma, since increasing distributed exploitation (allowing robots to exchange more routes) means decreasing distributed exploration (reducing robots ability to learn new versions of routes), and vice-versa. At different times, teams may be required with different balances of exploitation and exploration. The main contribution of this thesis is to present a system for setting the balance between exploration and exploitation in a group of robots. This system is demonstrated through experiments involving simulated robot teams. The experiments show that increasing and decreasing the value of a parameter of the novel system will lead to a significant increase and decrease respectively in average exploitation (and an equivalent decrease and increase in average exploration) over a series of team missions. A further set of experiments show that this holds true for a range of team sizes and numbers of goals

Creativity through autonomy and interaction

In this paper, we have sought to bring together several strands of our work, on motivation, autonomous agents and interaction between agents, to show how creativity can have a central place within what might be considered rather straightforward aspects of the design of modern computing systems. We review our previous work on the SMART agent framework and re-interpret it in the light of considerations of creativity arising from autonomy, motivation and contributing to the process of autonomous interaction. Here, behaviour is not prescribed but is determined in relation to motivation, leading to different, potentially creative outcomes for different individuals, especially during the process of interaction. Moreover, considering interaction as discovery imbues it with the same creative aspect as in scientific discovery, in which it can be argued that creativity plays a significant role in theory formation and revision. In fact, these are two sides of the same coin: in our view, the creativity in discovery arises from the motivation and autonomy of the individual involved

Contributions to an anthropological approach to the cultural adaptation of migrant agents

This thesis proposes the use of Cultural Anthropology as a source of inspiration for solutions to the problem of adaptation of autonomous, intelligent, computational agents that migrate to societies of agents with distinctive features from the ones of the society where those agents were originally conceived. This has implications for interoperation of disparate Multi-Agent Systems. In particular, the cognitive approach to anthropology is argued to be a suitable theoretical foun-dation for this topic. Fieldwork practice in social anthropology is also indicated as an useful source of ideas. A pragmatic theory of intensionality is incorporated in this anthropological approach, resulting in a mechanism that allows agents to ascribe intensional ontologies of terms to societies that use unfamiliar means of communication; also, taxonomical relations among the terms in such ontologies can be retrieved, by means of a process inspired by the counterpart activity of ethnographers. This is presented using the Z notation for formal specification of systems, and illustrated on a set of terms from the game of cricket. Subsequently, a simulation of a game of cricket is described where one of the players is unfamiliar with the game, and therefore needs to learn the game by observing the other players. A reasonable behaviour for such a player is obtained, and the simulation offers grounds for further anthropologically-based studies. Further, a study of theories of moral sentiments is presented, and the Iterated Prisoner's Dilemma is used in simulations based on those ideas. The results of the simulations show clearly the positive impact, on groups of agents, of altruistic behaviour; this can only be coherently obtained in autonomous agents by modelling emotions, which are relevant for this project as anthropologists recognise them as an essential cross-cultural link. Finally, the consequences of this project to conceptions of Distributed Artificial Intelligence are discussed

A Conceptual Framework for Agent Definition and Development

The use of agents of many different kinds in a variety of fields of computer science and artificial intelligence is increasing rapidly and is due, in part, to their wide applicability. The richness of the agent metaphor that leads to many different uses of the term is, however, both a strength and a weakness: its strength lies in the fact that it can be applied in very many different ways in many situations for different purposes; the weakness is that the term agent is now used so frequently that there is no commonly accepted notion of what it is that constitutes an agent. This paper addresses this issue by applying formal methods to provide a defining framework for agent systems. The Z specification language is used to provide an accessible and unified formal account of agent systems, allowing us to escape from the terminological chaos that surrounds agents. In particular, the framework precisely and unambiguously provides meanings for common concepts and terms, enables alternative models of particular classes of system to be described within it, and provides a foundation for subsequent development of increasingly more refined concepts

An agent-based model of energy in social networks

We present a family of simulation models of agents with energy from social interactions. We take the concept of “energy” from social network analysts Cross & Parker, from Collins’s micro-sociology of interaction rituals, and from the social psychologists Ryan & Deci’s studies on intrinsic motivation. We use simulation models as “tools for thinking” about what energy is, and how it relates to the take up of ideas, the formation of cultural groups and the performance of work. Our models also provide insight into phenomena from studies of “communities of practice”, social capital and computer models of networks. Baker & Quinn have also developed simulations of agents with energy, and so we offer a critique of those. We develop our models as extensions of the Axelrod Cultural Model. Our family of energy models include those that ascribe “emotional energy” variously to individual agents, to agents’ individual attributes, and to agents’ memories of interactions rituals. Agents obtain energy payoffs from interactions based variously on their sense of autonomy, belongingness and competence. We compare the behaviour of each model and choice of payoff function through experiments to test claims derived from Cross & Parker: namely that “energisers” cause greater take up of their ideas, cause larger cultural groups to form around them, and raise the problem-solving performance of the agent population. We demonstrate this first claim for several model scenarios, but fail to find scenarios where the second two hold. We then conduct experiments to relate the capabilities of energisers to tasks of: disseminating ideas to otherwise homogeneous groups, and; spanning boundaries across cultural divides between groups. In all experiments we find two factors play critical roles in determining the diffusion and homogenisation of culture: the decay of energy charges on memories, and; the initial number of cultural traits in the population

An agent-based model of energy in social networks

We present a family of simulation models of agents with energy from social interactions. We take the concept of “energy” from social network analysts Cross & Parker, from Collins’s micro-sociology of interaction rituals, and from the social psychologists Ryan & Deci’s studies on intrinsic motivation. We use simulation models as “tools for thinking” about what energy is, and how it relates to the take up of ideas, the formation of cultural groups and the performance of work. Our models also provide insight into phenomena from studies of “communities of practice”, social capital and computer models of networks. Baker & Quinn have also developed simulations of agents with energy, and so we offer a critique of those. We develop our models as extensions of the Axelrod Cultural Model. Our family of energy models include those that ascribe “emotional energy” variously to individual agents, to agents’ individual attributes, and to agents’ memories of interactions rituals. Agents obtain energy payoffs from interactions based variously on their sense of autonomy, belongingness and competence. We compare the behaviour of each model and choice of payoff function through experiments to test claims derived from Cross & Parker: namely that “energisers” cause greater take up of their ideas, cause larger cultural groups to form around them, and raise the problem-solving performance of the agent population. We demonstrate this first claim for several model scenarios, but fail to find scenarios where the second two hold. We then conduct experiments to relate the capabilities of energisers to tasks of: disseminating ideas to otherwise homogeneous groups, and; spanning boundaries across cultural divides between groups. In all experiments we find two factors play critical roles in determining the diffusion and homogenisation of culture: the decay of energy charges on memories, and; the initial number of cultural traits in the population.EThOS - Electronic Theses Online ServiceWarwick Business School (WBS)GBUnited Kingdo