Arguing Using Opponent Models

Peer reviewedPostprin

Properties for a formal model of collaborative dialogue

We propose a basic set of desirable properties for an abstract model of collaborative dialogue among agents. The abstraction comprehends the underlying logic of the agents, as well as the interaction protocol. The properties pursue the characterization of finite dialogues, with reasonable conclusions (based on what the participants have said), in which everything said is relevant and everything relevant is said. To this end, two levels of relevance (direct and potential ) are defined, based on the notions of inference and abduction, respectively. Illustrative examples, using mainly the DeLP formalism, are provided.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Adversarial Language Games for Advanced Natural Language Intelligence

We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker must hide its intention and subtly induce the defender, while a competitive defender must be cautious with its utterances and infer the intention of the attacker. Such language abilities can facilitate many important downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments and empirical studies on several baseline attack and defense strategies show promising and interesting results. Based on the analysis on the game and experiments, we discuss multiple promising directions for future research.Comment: Accepted by AAAI 202

On the interplay between games, argumentation and dialogues

Game theory, argumentation and dialogues all address problems concerning inter-agent interaction, but from different perspectives. In this paper, we contribute to the study of the interplay between these fields. In particular, we show that by mapping games in normal form into structured argumentation, computing dominant solutions and Nash equilibria is equivalent to computing admissible sets of arguments. Moreover, when agents lack complete information, computing dominant solutions/Nash equilibria is equivalent to constructing successful (argumentation-based) dialogues. Finally, we study agents’ behaviour in these dialogues in reverse game-theoretic terms and show that, using specific notions of utility, agents engaged in (argumentation-based) dialogues are guaranteed to be truthful and disclose relevant information, and thus can converge to dominant solutions/Nash equilibria of the original games even under incomplete information

The VERBMOBIL domain model version 1.0

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information contained in the model can be actually used in the VERBMOBIL demonstrator. In doing so, we also point out the main functionality of FLEX, the Description Logic system used for the modeling