Dominant decisions by argumentation agents

Artificial Intelligence, 2014

For Dung's model of abstract argumentation under preferred semantics, argumentation frameworks may have several distinct preferred extensions: i.e., in informal terms, sets of acceptable arguments. Thus the acceptance problem (for a specific argument) can consider deciding whether an argument is in at least one such extensions (credulously accepted) or in all such extensions (skeptically accepted). We start by presenting a new algorithm that enumerates all preferred extensions. Following this we build algorithms that decide the acceptance problem without requiring explicit enumeration of all extensions. We analyze the performance of our algorithms by comparing these to existing ones, and present experimental evidence that the new algorithms are more efficient with respect to the expected running time. Moreover, we extend our techniques to solve decision problems in a widely studied development of Dung's model: namely value-based argumentation frameworks (vafs). In this regard, we examine analogous notions to the problem of enumerating preferred extensions and present algorithms that decide subjective, respectively objective, acceptance.

Argument & Computation, 2021

Argumentation schemes [35,80,91] are a relatively recent notion that continues an extremely ancient debate on one of the foundations of human reasoning, human comprehension, and obviously human argumentation, i.e., the topics. To understand the revolutionary nature of Walton's work on this subject matter, it is necessary to place it in the debate that it continues and contributes to, namely a view of logic that is much broader than the formalistic perspective that has been adopted from the 20th century until nowadays. With his book Argumentation schemes for presumptive reasoning, Walton attempted to start a dialogue between three different fields or views on human reasoning-one (argumentation theory) very recent, one (dialectics) very ancient and with a very long tradition, and one (formal logic) relatively recent, but dominating in philosophy. Argumentation schemes were proposed as dialectical instruments, in the sense that they represented arguments not only as formal relations, but also as pragmatic inferences, as they at the same time depend on what the interlocutors share and accept in a given dialogical circumstance, and affect their dialogical relation. In this introduction, the notion of argumentation scheme will be analyzed in detail, showing its different dimensions and its defining features which make them an extremely useful instrument in Artificial Intelligence. This theoretical background will be followed by a literature review on the uses of the schemes in computing, aimed at identifying the most important areas and trends, the most promising proposals, and the directions of future research.

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Dung's abstract Argumentation Framework (AF) has emerged as a central formalism for argumentation in AI. Preferences in AF allow to represent the comparative strength of arguments in a simple yet expressive way. In this paper we first investigate the complexity of the verification as well as credulous and skeptical acceptance problems in Preference-based AF (PAF) that extends AF with preferences over arguments. Next, after introducing new semantics for AF where extensions are selected using cardinality (instead of set inclusion) criteria and investigating their complexity, we introduce a framework called AF with Priority rules (AFP) that extends AF with sequences of priority rules. AFP generalizes AF with classical set-inclusion and cardinality based semantics, suggesting that argumentation semantics can be viewed as ways to express priorities among extensions. Finally, we extend AFP by proposing AF with Priority rules and Preferences (AFP^2), where also preferences over argumen...

2005

In this research line we are exploring the possibility of having multiple preference criteria to compare arguments in in Defeasible Argumentation. In this work we are assuming a goaloriented Argumentative System, to which an application or an agent may seek an answer in response to a query. Thus, based on the current knowledge available, the system will decide whether there exists undefeated support for that query.

Proceedings of the ECAI

Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03, 2003

2002

PROforma is a language and a technology for designing, implementing, testing and delivering software agents that can operate in dynamic and uncertain environments. The agent specification language is based on R 2 L, a logic language that formalises reasoning, decisionmaking and plan execution using a combination of classical and non-classical logics. PROforma can be viewed as an object-oriented layer on top of R 2 L that reifies the logic into a small set of "tasks", notably decisions, plans and I/O tasks. The heart of decision-making in both PROforma and R 2 L is a logical decision model based on the creation and evaluation of arguments for and against alternative beliefs and actions. In previous papers we have provided proof-theoretic and model-theoretic semantics for a Logic of Argument and suggested that argumentation is a general framework which subsumes many different forms of uncertain reasoning as special cases, including qualitative and non-monotonic logics; quantitative representations like probability and possibility, and "linguistic" representations of belief. Current implementations support a simple monotonic interpretation of argument based decision-making but do not address requirements for defeasibility or probabilistic belief revision. In this paper we discuss how argumentation can provide a framework for integrating these approaches into a unified agent model.

Artificial Intelligence, 2009

The abstract nature of Dung's seminal theory of argumentation accounts for its widespread application as a general framework for various species of non-monotonic reasoning, and, more generally, reasoning in the presence of conflict. A Dung argumentation framework is instantiated by arguments and a binary conflict based attack relation, defined by some underlying logical theory. The justified arguments under different extensional semantics are then evaluated, and the claims of these arguments define the inferences of the underlying theory. To determine a unique set of justified arguments often requires a preference relation on arguments to determine the success of attacks between arguments. However, preference information is often itself defeasible, conflicting and so subject to argumentation. Hence, in this paper we extend Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level. We then define and study application of the full range of Dung's extensional semantics to the extended framework, and study special classes of the extended framework. The extended theory preserves the abstract nature of Dung's approach, thus aiming at a general framework for non-monotonic formalisms that accommodate defeasible reasoning about as well as with preference information. We illustrate by formalising argument based logic programming with defeasible priorities in the extended theory.

Decision Aiding can be abstractly described as the process of assisting a user/client/decision maker by recommending possible courses of his action. This process has to be able to cope with incomplete and/or inconsistent information for the following reasons. First, the recommendations provided during this process depend heavily on the environment the decision is made. Since complete knowledge of this environment is almost impossible, decision aiding has to be carried out under incomplete information. Second, the decision aiding process also depends on the preferences of its user. However, such subjective information is affected by uncertainty, possible inconsistencies and is dynamically revised due to the time dimension of decision aiding. A complete description of a model of the user is also almost impossible, therefore a decision aiding process must also account for this source of incompleteness. This paper presents a model of Decision Aiding that is amenable to automation and shows how it can be embedded in autonomous agents and thus give them the capability to provide decision aiding to a human user or to completely substitute him for some decision task. The whole process is modelled in an suitable argumentation framework that treats Decision Aiding as an iterative defeasible reasoning process.