Arguing and negotiating in the presence of social influences

Postprin

Reconsidering RepStat rules in dialectic games.

Prohibition of repeated statements has benefits for the tractability and predictability of dialogues carried out by machines, but doesn't match the real world behaviour of people. This gap between human and machine behaviour leads to problems when formal dialectical systems are applied in conversational AI contexts. However, the problem of handling statement repetition gives insight into wider issues that stem partly from the historical focus on formal dialectics to the near exclusion of descriptive dialectics. In this paper we consider the problem of balancing the needs of machines versus those of human participants through the consideration of both descriptive and formal dialectics integrated within a single overarching dialectical system. We describe how this approach can be supported through minimal extension of the Dialogue Game Description Language

Supporting shape reinterpretation with eye tracking

It has been argued that reinterpretation is an essential process in design generation and idea exploration. However, computational design tools, such as computer-aided design systems, offer poor support for shape reinterpretation, and as such are not well suited to ideation in conceptual design. One of the key difficulties in implementing computational systems that support shape reinterpretation is the issue of interface – how can a user intuitively guide a system with respect to their interpretation of a designed shape? In this paper, a software prototype is presented that uses an eye tracking interface to support reinterpretation of shapes according to recognised subshapes. The prototype is based on eye tracking studies, and uses gaze data and user input to restructure designed shapes so that they afford manipulation according to users’ interpretations

Modeling multiagent deliberation from an abstract standpoint

Simply put, a multiagent system can be understood as a collection of autonomous agents able to accomplish as a whole goals beyond the capabilities of any of its members. The traditional example depicts a heavy armchair that can be easily lifted by coordinating the effort of a group of persons despite that none of them would have been able to pick it up alone. Thus, one might argue that precisely the agent interaction is boosting the system performance. Since this interaction comes in several flavors, the literature has already explored notions such as agent coordination, cooperation, and collaboration in the context of multiagent systems. This extended abstract outlines our own understanding on this matter, summarizing the evolution of an abstract model for the particular kind of agent interaction known as deliberation. A group of agents deliberate whenever they need to come to a mutually accepted position about some issue. This interaction among agents has drawn our attention given its ubiquity: we believe that complex interactions such as coordination or cooperation might be attained as a result of accruing one or more deliberations. Our proposal is inspired after the novel trend of reinterpreting agent interaction as if it were the result of an argumentation process. For instance, several authors [2,3,5,13,14] have recently considered recasting the main aspects of multiagent negotiation in terms of defeasible argumentation. We follow a like approach in developing our model after a set of dialectical concepts borrowed from that same area. Our approach also strives for generality, mainly after Dung's ample success with his notion of argumentative framework due to its abstract nature. In consequence, we too have decided to pursue an abstract model.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

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

On the use of contexts for representing knowledge in defeasible argumentation

The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI