Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes

Fuzzy Logic Based Negotiation in E-Commerce

The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

A theoretical framework for computer models of cooperative dialogue, acknowledging multi-agent conflict

This thesis describes a theoretical framework for modelling cooperative dialogue. The linguistic theory is a version of speech act theory adopted from Cohen and Levesque, in which dialogue utterances are generated and interpreted pragmatically in the context of a theory of rational interaction. The latter is expressed as explicitly and formally represented principles of rational agenthood and cooperative interaction. The focus is the development of strategic principles of multi-agent interaction as such a basis for cooperative dialogue. In contrast to the majority of existing work, these acknowledge the Positive role of conflict to multi-agent cooperation. and make no assumptions regarding the benevolence and sincerity of agents. The result is a framework wherein agents can resolve conflicts by negotiation. It is a preliminary stage to the future building of computer models of cooperative dialogue for both HCI and DAI, which will therefore be more widely and generally applicable than those currently in existence. The theory of conflict and cooperation is expressed in the different patterns of mental states which characterise multi-agent conflict, cooperation and indifference as three alternative postural relations. Agents can recognise and potentially create these. Dialogue actions are the strategic tools with which mental states can be manipulated, whilst acknowledging that agents are autonomous over their mental states; they have control over what they acquire and reveal in dialogue. Strategic principles of belief and goal adoption are described in terms of the relationships between autonomous agents' beliefs, goals, preferences, and interests, and the relation of these to action. Veracity, mendacity, concealing and revealing are defined as properties of acts. The role of all these elements in reasoning about dialogue action and conflict resolution, is tester in analyses of two example dialogues; a record of a real trade union negotiation and an extract from "Othello" by Shakespeare

The Effects of IT, Task, Workgroup, and Knowledge Factors on Workgroup Outcomes: A Longitudinal Investigation

In order to successfully manage the knowledge-related processes occurring in their workgroups, organizations need to understand how different contingency factors affect the knowledge-related processes of a workgroup, ultimately affecting the workgroup\u27s knowledge outcomes and performance. To obtain a deeper understanding of the longitudinal effects of different contingency factors on knowledge outcomes and performance of workgroups, this dissertation was guided by the research question: Which factors, from the five categories of factors (a) characteristics of the workgroup; (b) characteristics of the tasks assigned to the workgroup; (c) the interface between the workgroup and the tasks; (d) characteristics of the knowledge required to complete the tasks; and (e) characteristics of the information technologies, affect workgroup outcomes, including (i) average consensus among a workgroup\u27s members about each other\u27s areas of knowledge; (ii) average accuracy of knowledge; and (iii) performance of the workgroup, over time, and in what way? Workgroup processes considered were categorized into three groups: processes related to scheduling of tasks, processes related to completion of tasks and processes accompanying those related to completion of tasks. Results indicate that only a subset of contingency factors from each category affect each of the workgroup outcomes. Specifically, average task priority, average knowledge-intensity of subtasks, average propensity to share, time in training phase, probability of non-specific exchange, number of agents, number of locations and average project intensity were found to have a positive effect on average consensus, while average task intensity, average self-knowledge and average number of tasks per agent had negative effect on average consensus. In the case of average accuracy of knowledge, average knowledge level and number of agents were found to have a positive significant effect. Finally, in the case of percentage of project completed, average propensity to share, average knowledge level, average self-knowledge, and time in training phase were found to have a positive significant effect, while average knowledge intensity of subtasks, richness of email, and average direction time were found to have a negative significant effect. Average number of tasks per agent was found to have a significant negative effect between workgroups and positive significant effect within workgroups

On Agent Communication in Large Groups

The problem is fundamental and natural, yet deep - to simulate the simplest possible form of communication that can occur within a large multi-agent system. It would be prohibitive to try and survey all of the research on communication in general so we must restrict our focus. We will devote our efforts to synthetic communication occurring within large groups. In particular, we would like to discover a model for communication that will serve as an abstract model, a prototype, for simulating communication within large groups of biological organisms

A case study of agent programmability in an online learning environment

Software agents are well-suited to assisting users with routine, repetitive, and time-consuming tasks in various educational environments. In order to achieve complex tasks effectively, humans and agents sometimes need to work together. However, some issues in human agent interaction have not been solved properly, such as delegation, trust and privacy. The agent research community has focused on technologies for constructing autonomous agents and techniques for collaboration among agents. Little attention has been paid to supporting interactions between humans and agents. p* The objectives of this research are to investigate how easy it might be for a user to program his/her agent, how users behave when given the ability to program their agents, whether access to necessary help resources can be improved, and whether such a system can facilitate collaborative learning. Studying users’ concerns about their privacy and how an online learning environment can be built to protect users’ privacy are also interesting issues to us. In this thesis two alternative systems were developed for programmable agents in which a human user can define a set of rules to direct an agent’s activities at execution time. The systems were built on top of a multi-agent collaborative learning environment that enables a user to program his or her agent to communicate with other agents and to monitor the activities of other users and their agents. These systems for end user programmable agents were evaluated and compared. The result demonstrated that an end-user programming environment is able to meet users’ individual needs on awareness information, facilitate the information exchange among the users, and enhance the communication between users within a virtual learning environment. This research provides a platform for investigating concerns over user privacy caused by agent programmability

Dynamic enterprise modelling: a methodology for animating dynamic social networks

PhD ThesisSince the introduction of the Internet and the realisation of its potential companies have either transformed their operation or are in the process of doing so. It has been observed, that developments in I.T., telecommunications and the Internet have boosted the number of enterprises engaging into e-commerce, e-business and virtual enterprising. These trends are accompanied by re-shaping, transformation and changes in an enterprise's boundaries. The thesis gives an account of the research into the area of dynamic enterprise modelling and provides a modelling methodology that allows different roles and business models to be tested and evaluated without the risk associated with committing to a change

The Social Cognitive Actor

Multi-Agent Simulation (MAS) of organisations is a methodology that is adopted in this dissertation in order to study and understand human behaviour in organisations. The aim of the research is to design and implementat a cognitive and social multi-agent simulation model based on a selection of social and cognitive theories to fulfill the need for a complex cognitive and social model. The emphasis of this dissertation is the relationship between behaviour of individuals (micro-level) in an organisation and the behaviour of the organisation as a whole (macro-level)