Argumentation-based Negotiation Research Articles

Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.

Arguing and negotiating using incomplete negotiators profiles

Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles. After the agent identifies its best option, in any state of a negotiation, it looks for suitable arguments that support this option in the theory of its opponent. As the knowledge on the opponent is uncertain, the challenge is to find arguments that, ideally, support the selected option despite the uncertainty. We present a negotiation framework based on these ideas, along with experimental evidence that highlights the advantages of our approach.

Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System

Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems often do not have enough of such information in realistic situations. One way to fill in the gap is acquiring such missing information from dialogue partners (information-seeking dialogue). Existing information-seeking dialogue systems are based on handcrafted dialogue strategies that exhaustively examine missing information. However, the proposed strategies are not specialized in collecting information for constructing rational arguments. Moreover, the number of system's inquiry candidates grows in accordance with the size of the argument set that the system deal with. In this paper, we formalize the process of information-seeking dialogue as Markov decision processes (MDPs) and apply deep reinforcement learning (DRL) for automatically optimizing a dialogue strategy. By utilizing DRL, our dialogue strategy can successfully minimize objective functions, the number of turns it takes for our system to collect necessary information in a dialogue. We conducted dialogue experiments using two datasets from different domains of argumentative dialogue. Experimental results show that the proposed formalization based on MDP works well, and the policy optimized by DRL outperformed existing heuristic dialogue strategies.

A Study of Integrative Bargaining Model with Argumentation-Based Negotiation

E-commerce is increasingly competitive and there is a constant need for new approaches and technology to facilitate exchange. Emerging techniques include the use of artificial intelligence (AI). One AI tool that has sparked interest in e-commerce is the automated negotiation agent (negotiation-agent). This study examines such agents, and proposes an offer strategy model of integrative negotiation for a negotiation-agent with a focus on negotiation agent-to-human interaction. More specifically, a new offer strategy was developed based on the integrative bargaining model, which emphasizes the importance of exchanging information among negotiators and multi-issue negotiation that includes package offers to achieve an integrative (win-win) outcome. This study incorporated an argumentation-based negotiation and the negotiation tactic of multiple equivalent simultaneous offers, which was programmed into the negotiation-agent. An experiment was conducted performing 49 negotiation-agent-to-human negotiations over three issues in online purchase tasks to demonstrate the effectiveness of the proposed strategy. Experimental results indicated that the proposed offer strategy with agent negotiation can enhance the persuasiveness of an offer and the performance of negotiation outcome (human counterpart’s perception toward negotiation process, opponent–agent and desire for future negotiation). The findings confirmed the effectiveness of the proposed design and demonstrated an innovative approach to e-commerce transactions.

A Collaborative Decision Support Tool for Managing Chronic Conditions.

This paper describes work to assess the feasibility of using a decision support tool to help patients with chronic conditions, specifically stroke, manage their condition in collaboration with their carers and the health care professionals who are looking after them. The system contains several novel elements: the integration of data from commercial wellness sensors, electronic health records and clinical guidelines; the use of computational argumentation to track the source of data and to resolve conflicts and make recommendations; and argumentation-based dialogue to support interaction with patients. The proposed approach is implemented as an application that can run on smart devices (e.g. tablets). The users have personalised dashboards where they can visualise their health data and interact with a conversational chatbot that provides further explanations about their overall well-being.

PCA-I and AHP Methods: Unavoidable Arguments in Accident Scenario Classification

Risk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.

Arguing with Behavior Influence: A Model for Web-Based Group Decision Support Systems

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers’ intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents’ behavior style. The inclusion of decision-makers’ intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers’ intentions.

Reasoning and communicative strategies in a model of argument-based negotiation

ABSTRACTWe are developing a model of negotiation in a natural language where the communicative goal of the initiator is to achieve the decision of the communication partner about doing a certain action. In order to make a decision, the partner starts a reasoning process checking the existence of the needed resources as well as the positive and negative aspects of doing the proposed action. If the partner does not make the expected decision then in the following communication, arguments for and against of doing the action will be presented by the participants. We discuss the involvement of a reasoning model as well as the notions of communicative strategy and communicative tactics in the negotiation model. We consider how the partners can influence each other when reasoning in order to achieve their communicative goals. A limited version of the model of negotiation is implemented on the computer. We discuss some implementation details.

Towards a Group Decision Support System

This research focuses on the design and development of an interactive multi-criteria group decision support system (GDSS). This system comprises a set of agents communicating through messages. They aim to find an adequate area that can best address certain criteria among a set of variants (e.g. localization of new infrastructure, shopping mall, hospital, etc.). A GDSS is designed and relies mainly on the presentation of the territory through the functionalities of geographic information system (GIS), and the representation of the multiplicity and diversity of decision makers through the functionalities of a multi-agents system (MAS). Moreover, an argumentation-based negotiation protocol MAS module is provided. This approach is justified by the argumentative nature of the process by which participants justify their choices in order to reach a collective decision. This is achieved by exploiting the multi-criteria methods with an analytic hierarchy process (AHP) and Electre III.

Assessing communication strategies in argumentation-based negotiation agents equipped with belief revision1

Fil: Casali, Ana. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Informacion y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Informacion y de Sistemas; Argentina

An Argumentation-Based Negotiation Approach in Electronic Marketplace based on Semantic Web

The argumentation-based negotiation is studied in several research works, the majority of them propose general models. The modeling of argumentation-based negotiation mechanism for a domain, such as e-commerce takes into account its specificity with regard to the nature of exchanged information. This study aimed to propose a new approach of argumentation-based negotiation in e-commerce which synthesize and improve the general approaches of argumentation. The model of argument is studied, for the use of its elements and structure. The force field analysis technique is improved to represent the various forces which conduct decisions taken during a dialog of negotiation. Semantic Web technologies are employed for the representation of the arguments, forces conducting agents' decisions, as well as the protocol of negotiation. Semantic web technologies are also employed in the representation of all knowledge which relates to marketplace and its elements (products, buyer and sellers). This study is supported by an agent based modeling of an electronic marketplace, in which a set of agents is developed and a set of ontology is created. Laptops are taken as a case of study of sold products. In addition, an argument interchange mechanism is implemented to transfer the arguments represented by OWL-DL sub-language into the content of FIPA-ACL messages. This modeling forms part to a work of development for the validation of the proposed approach.

The Automated Negotiating Agents Competition, 2010–2015

The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.

A belief revision approach for argumentation-based negotiation agents

Abstract Negotiation is an interaction that happens in multi-agent systems when agents have conflicting objectives and must look for an acceptable agreement. A typical negotiating situation involves two agents that cannot reach their goals by themselves because they do not have some resources they need or they do not know how to use them to reach their goals. Therefore, they must start a negotiation dialogue, taking also into account that they might have incomplete or wrong beliefs about the other agent’s goals and resources. This article presents a negotiating agent model based on argumentation, which is used by the agents to reason on how to exchange resources and knowledge in order to achieve their goals. Agents that negotiate have incomplete beliefs about the others, so that the exchange of arguments gives them information that makes it possible to update their beliefs. In order to formalize their proposals in a negotiation setting, the agents must be able to generate, select and evaluate arguments associated with such offers, updating their mental state accordingly. In our approach, we will focus on an argumentation-based negotiation model between two cooperative agents. The arguments generation and interpretation process is based on belief change operations (expansions, contractions and revisions), and the selection process is a based on a strategy. This approach is presented through a high-level algorithm implemented in logic programming. We show various theoretical properties associated with this approach, which have been formalized and proved using Coq, a formal proof management system. We also illustrate, through a case study, the applicability of our approach in order to solve a slightly modified version of the well-known home improvement agents problem. Moreover, we present various simulations that allow assessing the impact of belief revision on the negotiation process.

Argumentation-Based Dialogue Games for Shared Control in Human-Robot Systems

Dialogue can support exchange of ideas and discussion of options as a means to enable shared decision making for human-robot collaboration. However, dialogue that supports dynamic, evidence-backed exchange of ideas is a major challenge for today's human-robot systems. The work presented here investigates the application of argumentation-based dialogue games as the means to facilitate flexible interaction, including unscripted changes in initiative. Two main contributions are provided in this paper. First, a methodology for implementing multiple types of argumentation-based dialogues for human-robot interaction is detailed. This includes explanation about which types of dialogues are appropriate given the beliefs of the participants and how multiple dialogues can occur simultaneously while maintaining a consistent set of beliefs for the participants. Second, a formal definition is presented for the Treasure Hunt Game (THG), a test environment that provides rich opportunities for experimentation in shared human-robot control, as well as motivating and engaging experiences for human subjects.

An Improved Multi-agent Communication Model Based on Debate Negotiation

The paper studies the argumentation-based negotiation technology of multi-agent, improves the multiagent communication model by combining argumentation and minimum spanning tree algorithm, and designs the algorithm about selecting optimal path of center agent, that raises the adaptive ability and work e‐ciency of the model.

A general framework for sound assumption-based argumentation dialogues

We propose a formal model for argumentation-based dialogues between agents, using assumption-based argumentation (ABA) as the underlying argumentation framework. Thus, the dialogues amount to conducting an argumentation process in ABA. The model is given in terms of ABA-specific utterances, debate trees and forests implicitly built during and drawn from dialogues, legal-move functions (amounting to protocols) and outcome functions. The model is generic in that it is not restricted to any specific dialogue types and can be used to support a wide range thereof. We prove a formal connection between dialogues and three well-known argumentation semantics (i.e. grounded, admissible and ideal extensions), by giving soundness results for our dialogue models with respect to these semantics. Thus, our dialogues can be seen as a distributed mechanism for successfully determining acceptability of claims (with respect to the semantics considered), while constructing argumentation frameworks and arguments for these claims.

Strategic Argumentation in Multi-Agent Systems

Argumentation-based negotiation describes the process of decision-making in multi-agent systems through the exchange of arguments. If agents only have partial knowledge about the subject of a dialogue strategic argumentation can be used to exploit weaknesses in the argumentation of other agents and thus to persuade other agents of a specific opinion and reach a certain outcome. This paper gives an overview of the field of strategic argumentation and surveys recent works and developments. We provide a general discussion of the problem of strategic argumentation in multi-agent settings and discuss approaches to strategic argumentation, in particular strategies based on opponent models.

Agents’ Uncertainty in Argumentation-based Negotiation: Classification and Implementation

In this paper, we tackle the problem of uncertainty in argumentation-based agent negotiation by analyzing the different situations of this uncertainty. In particular, we analyze two types of agents’ uncertainty called Type I and Type II. Uncertainty Type I is the agent's uncertainty about selecting the right moves during the dialogue. Uncertainty Type II is the agent's uncertainty that the selected move will be accepted by the addressee. More precisely, we discuss these uncertainties in two cases based on the different classes that arguments can belong to. In addition to the theoretical analysis of arguments uncertainty, we implemented the proposed approach by applying it on a concrete case study (Buyer/Seller scenario). The obtained empirical results confirm the effectiveness of using our uncertainty-aware techniques and show that our negotiating agents outperform others (which do not use such techniques). We believe that such analysis will advance the research in the area of argumentation-based negotiation in multiagent systems and contribute to the automation of the agents’ negotiation.

Measuring and analyzing agents’ uncertainty in argumentation-based negotiation dialogue games

Nowadays, multiagent systems have become a widely used technology in everyday life, and many authors have adopted the view of communication or interaction between agents as a joint activity regulated by means of dialogue games. Dialogue games are a set of communication rules that agents can combine in their complex interactions. In these games, uncertainty is an important problem that each agent faces when making decisions, especially in the absence of enough information. This paper focuses on the uncertainty in a particular type of dialogue games, namely argumentation-based negotiation. There exist several proposals on this type of dialogue games in the literature, and most of them are concerned with proposing protocols to show how agents can communicate with each other, and how arguments and offers can be generated, evaluated and exchanged. Nevertheless, none of them is directly targeting the agents’ uncertainty about the exchanged arguments and how this uncertainty could be measured at each dialogue step to assist those agents make better decisions. The aim of this paper is to tackle this problem by defining a new set of uncertainty measures in negotiation dialogue games from an external agent’s point of view. In particular, we introduce two types of uncertainty: Type I and Type II. Type I is about the uncertainty index of playing the right move. For this, we use Shannon entropy to measure: (i) the uncertainty index of the agent that he is selecting the right move at each dialogue step; and (ii) the uncertainty index of participating agents in the negotiation about the whole dialogue. This is done in two different ways; the first is by taking the average of the uncertainty index of all moves, and the second is by determining all possible dialogues and applying the general formula of Shannon entropy. Type II is about the uncertainty degree of the agent that the move will be accepted by the addressee. In this context, we introduce a new classification for the arguments based on their certainty to be accepted by the addressee.

A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation

Deciding what argument to utter during a negotiation is a key part of the strategy to reach an expected agreement. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. By following this policy, the agent observes some factors of the negotiation context (for instance, trust in the opponent and expected utility of the negotiated agreement). Usually, argument selection policies are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection policy is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the different negotiation contexts as the agent gains experience. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection effectiveness by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment. We obtained promising results in both.