Multi-agent Argumentation Research Articles

An Analysis of Multi Agent Systems Agent Based Programming

The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.

A Review of Agent-Based Programming for Multi-Agent Systems

Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.

Epistemic Argumentation Framework: Theory and Computation

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses complexity issues and computation using epistemic logic programming.

Plausible Argumentation in Eikotic Arguments: The Ancient Weak versus Strong Man Example

In this paper it is shown how plausible reasoning of the kind illustrated in the ancient Greek example of the weak and strong man can be analyzed and evaluated using a procedure in which the pro evidence is weighed against the con evidence using formal, computational argumentation tools. It is shown by means of this famous example how plausible reasoning is based on an audience’s recognition of situations of a type they are familiar with as normal and comprehensible in their shared common knowledge. The paper extends previous work on this example by using three new multiagent argumentation schemes closely related to the scheme for argument from negative consequences.

Plausible Argumentation in Eikotic Arguments: The Ancient Weak Versus Strong Man Example

In this paper it is shown how plausible reasoning of the kind illustrated in the ancient Greek example of the weak and strong man can be analyzed and evaluated using a procedure in which the pro evidence is weighed against the con evidence using formal, computational argumentation tools. It is shown by means of this famous example how plausible reasoning is based on an audience’s recognition of situations of a type they are familiar with as normal and comprehensible in their shared common knowledge. The paper extends previous work on this example by using three new multiagent argumentation schemes closely related to the scheme for argument from negative consequences.

Argumentation based joint learning: a novel ensemble learning approach.

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.

Symbolic Verification of a Strength Based Multi-Agent Argumentation Scheme

Software systems have evolved to the age of Artificial Intelligence (AI), consist- ing of independent autonomous agents interacting with each other in dynamic and unpredictable environments. In this kind of environment it is often very difficult to predict all the interactions between the agents. Hence verification of an interaction between multiple agents has become a key research area in AI. In this paper we model and verify an Automatic Meeting Scheduling (AMS) problem having multiple agent communication. The AMS problem helps us to emulate a real life scenario where multiple agents can argue over the defined constraints. A weighted strength based argu- mentation scheme is proposed, where each argument is weighed against each other to determine the strongest evidence. The argumentation model described in the paper have six agents: Initiator, Scheduler and four Participant agents. We have formalized the agent interactions using Computation Tree Logic (CTL) and verified the scheme by providing suitable specifications (SPEC) in a symbolic model verifier tool called, NuSMV.

Towards a visualization of arguing agents

In this paper, we show how to visualize arguing agents. We describe a visualization of single-agent and multi-agent argumentation. In the former, we show how to visualize the argumentation process and space within one agent, in the latter we develop an animation of communicating agents and map the relations between agents to spatial distance. We discuss the advantages/disadvantages of our solutions and compare it to alternatives.

Ethotic arguments and fallacies: The credibility function in multi-agent dialogue systems

In this paper, it is shown how formal dialectic can be extended to model multi-agent argumentation in which each participant is an agent. An agent is viewed as a participant in a dialogue who not only has goals, and the capability for actions, but who also has stable characteristics of types that can be relevant to an assessment of some of her arguments used in that dialogue. When agents engage in argumentation in dialogues, each agent has a credibility function that can be adjusted upwards or downwards by certain types of arguments brought forward by the other agent in the dialogue. One type is the argument against the person or argumentum ad hominem, in which personal attack on one party's character is used to attack his argument. Another is the appeal to expert opinion, traditionally associated with the informal fallacy called the argumentum ad verecundiam. In any particular case, an agent will begin a dialogue with a given degree of credibility, and what is here called the credibility function will affect the plausibility of the arguments put forward by that agent. In this paper, an agent is shown to have specific character traits that are vital to properly judging how this credibility function should affect the plausibility of her arguments, including veracity, prudence, sincerity and openness to opposed arguments. When one of these traits is a relevant basis for an adjustment in a credibility function, there is a shift to a subdialogue in which the argumentation in the case is re-evaluated. In such a case, it is shown how the outcome can legitimately be a reduction in the credibility rating of the arguer who was attacked. Then it is shown how the credibility function should be brought into an argument evaluation in the case, yielding the outcome that the argument is assigned a lower plausibility value.