AGENT-BASED NEGOTIATION PLATFORM IN COLLABORATIVE NETWORKED ENVIRONMENT

This paper proposes an agent-based platform to model and support parallel and concurrent negotiations among organizations acting in the same industrial market. The underlying complexity is to model the dynamic environment where multi-attribute and multi-participant negotiations are racing over a set of heterogeneous resources. The metaphor Interaction Abstract Machines (IAMs) is used to model the parallelism and the non-deterministic aspects of the negotiation processes that occur in Collaborative Networked Environment

Adapting a Kidney Exchange Algorithm to Align with Human Values

The efficient and fair allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who gets what--and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments

Formal Assurance Arguments: A Solution In Search of a Problem?

An assurance case comprises evidence and argument showing how that evidence supports assurance claims (e.g., about safety or security). It is unsurprising that some computer scientists have proposed formalizing assurance arguments: most associate formality with rigor. But while engineers can sometimes prove that source code refines a formal specification, it is not clear that formalization will improve assurance arguments or that this benefit is worth its cost. For example, formalization might reduce the benefits of argumentation by limiting the audience to people who can read formal logic. In this paper, we present (1) a systematic survey of the literature surrounding formal assurance arguments, (2) an analysis of errors that formalism can help to eliminate, (3) a discussion of existing evidence, and (4) suggestions for experimental work to definitively answer the question

EQRbot: A chatbot delivering EQR argument-based explanations

Data availability statement: The provided link: https://github.com/FCast07/EQRbot refers to the GitHub repository that stores the chatbot programming code.Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents.This research was partially funded by the UK Engineering & Physical Sciences Research Council (EPSRC) under Grant #EP/P010105/1

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

EQRbot: A chatbot delivering EQR argument-based explanations

Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents

In memoriam Douglas N. Walton: the influence of Doug Walton on AI and law

Doug Walton, who died in January 2020, was a prolific author whose work in informal logic and argumentation had a profound influence on Artificial Intelligence, including Artificial Intelligence and Law. He was also very interested in interdisciplinary work, and a frequent and generous collaborator. In this paper seven leading researchers in AI and Law, all past programme chairs of the International Conference on AI and Law who have worked with him, describe his influence on their work