A Mathematical Model of Dialog

AbstractComputer Science is currently undergoing a paradigm shift, from viewing computer systems as isolated programs to viewing them as dynamic multi-agent societies. Evidence of this shift is the significant effort devoted recently to the design and implementation of languages and protocols for communications and interaction between software agents. Despite this effort, no formal mathematical theory of agent interaction languages and protocols yet exists. We argue that such a theory needs to account for the semantics of agent interaction, and propose the first mathematical theory which does this. Our framework incorporates category-theoretic entities for the utterances made in an agent dialog and for the commitments incurred by those utterances, together with maps between these

Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

An Infrastructure for Argumentative Agents

Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.This work is supported by the Spanish government grants CONSOLIDER INGENIO 2010 CSD2007-00022, MINECO/FEDER TIN2012-36586-C03-01, and TIN2011-27652-C03-01.Jordan Prunera, JM.; Heras Barberá, SM.; Valero Cubas, S.; Julian Inglada, VJ. (2014). An Infrastructure for Argumentative Agents. Computational Intelligence. 31(3):418-441. doi:10.1111/coin.12030S41844131

Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them to those of virtue agents

Argument-based agreements in agent societies

In this paper, we present an abstract argumentation framework for the support of agreement processes in agent societies. It takes into account arguments, attacks among them, and the social context of the agents that put forward arguments. Then, we de¿ne the semantics of the framework, providing a mechanism to evaluate arguments in view of other arguments posed in the argumentation process. We also provide a translation of the framework into a neural network that computes the set of acceptable arguments and can be tuned to give more or less importance to argument attacks. Finally, the framework is illustrated with an example in a real domain of a water-rights transfer market. & 2011 Elsevier B.V. All rights reservedThis work is supported by the Spanish government Grants CONSOLIDER INGENIO 2010 CSD2007-00022, TIN2008-04446 and TIN2009-13839-C03-01 and by the GVA project PROMETEO 2008/051.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2012). Argument-based agreements in agent societies. Neurocomputing. 75(1):156-162. doi:10.1016/j.neucom.2011.02.022S15616275

Agent based decision support systems in medicine

Embedding Machine Learning technology into Agent Driven Diagnosis Systems adds a new potential to the realm of Medicine, and in particular to the imagiology one. However, despite all the research done in the last years on the development of new methodologies for problem solving, in terms of the design of MultiAgent Systems (MAS) there is none where both the agent and the organizational view can be modelled. Current multi-agent approaches to problem solving either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the behaviour of the agent society as a whole and/or the actions of the individuals. In this paper, it is proposed not only a framework for modelling and run agent organizations, but also to depict the different components of such societies. To illustrate these premises, we will evoke a society with one modality, the Axial Computed Tomography one, where two different but complementary computational paradigms, the Artificial Neural Networks and the Case Based Reasoning are object of attention

Creating artificial societies through interaction analysis: Translating qualitative observational study into agent-based modelling

The use of interaction analysis for creating agent-based models could lead to more empirical simulations. This study focuses on pedestrian-street interaction during negotiations at a signal-controlled crossing. It examines which data from interaction analysis could be used in the development of artificial pedestrian societies. This article sets out a framework for structuring the data deriving from interaction analysis and demonstrates the process of developing an agent-based model by translating the acquired data into the model. The data collected through interaction analysis serves as input for the agent-based model. The structuring performed on the interaction analysis data is used to define the outcome variables for agent-based modelling. The study concludes by proposing an initial framework that describes the use of interaction analysis in simulations

Unifying control in a layered agent architecture

In this paper, we set up a unifying perspective of the individual control layers of the architecture InteRRaP for autonomous interacting agents. InteRRaP is a pragmatic approach to designing complex dynamic agent societies, e.g. for robotics Müller & Pischel and cooperative scheduling applications Fischer et al.94. It is based on three general functions describing how the actions an agent commits to are derived from its perception and from its mental model: belief revision and abstraction, situation recognition and goal activation, and planning and scheduling. It is argued that each InteRRaP control layer - the behaviour-based layer, the local planning layer, and the cooperative planning layer - can be described by a combination of different instantiations of these control functions. The basic structure of a control layer is defined. The individual functions and their implementation in the different layers are outlined. We demonstrate various options for the design of interacting agents within this framework by means of an interacting robots application. The performance of different agent types in a multiagent environment is empirically evaluated by a series of experiments