Defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning from incomplete and potentially inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible argumentation formalism based on an extension of logic programming. Although DeLP has been successfully integrated in a number of different real-world applications, DeLP cannot deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper introduces P-DeLP, a new logic programming language that extends original DeLP capabilities for qualitative reasoning by incorporating the treatment of possibilistic uncertainty and fuzzy knowledge. Such features will be formalized on the basis of PGL, a possibilistic logic based on Gödel fuzzy logic.

Argumentation is a natural meaning of reasoning in the daily life, and has also become a highly interested topic of knowledge representation in the past few years. In this paper, we will use the phrase "two-sided argumentation" for a type of formalization for our real world debate: an issue with a pro-side supports it and a con-side opposes it. Then, we will point out that, when we use the term "argumentation," we in fact mean a binary concept: a method of reasoning, and a type of negotiation. For both case, we will consider the semantics: argumentative models for the former, argumentation games for the latter. We will also give out some results about the relationship between them.

The area of argumentation theory is an increasingly important area of artificial intelligence and mechanisms that are able to automatically detect the argument structure provide a novel area of research. This paper considers the use of kernel methods for argumentation detection and classification. It shows that a classification accuracy of 65%, can be attained using Natural Language Processing based kernel approaches, which do not require any heuristic choice of features.

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

We describe the application of assumption-based argumentation (ABA) to a domain of medical knowledge derived from clinical trials of drugs for breast cancer. We adapt an algorithm for calculating the admissible semantics for ABA frameworks to take account of preferences and describe a prototype implementation which uses variant-based parallel computation to improve the efficiency of query answering.

Recently, (Dunne et al. 2009; 2011) have suggested to weight attacks within Dung’s abstract argumentation frameworks, and introduced the concept of WAF (Weighted Argumentation Framework). However, they use WAFs in a very specific way for relaxing attacks. The aim of this paper is to explore ways to take advantage of attacks weights within an argumentation process. Two different approaches are considered: The first one extends the proposal by (Dunne et al. 2011) and accounts for other aggregation functions than sum in the objective of relaxing attacks. The second one shows how weights can be exploited to strengthen the usual notion of defence, leading to new concepts of extensions.

Argumentation is a human-like reasoning mechanism contributing to the formalization of commonsense reasoning. In the last decade, several argument-based formalisms have emerged, with application in many areas, such as legal reasoning, autonomous agents and multi-agent systems; many are based on Dung’s seminal work characterizing Abstract Argumentation Frameworks (AF). Recent research in the area has led to Temporal Argumentation Frameworks (TAF) that extend Dung’s by considering the temporal availability of arguments. In this work we introduce a novel framework, called Extended Temporal Argumentation Framework (E-TAF), extending TAF with the capability of modeling availability of attacks among arguments, which allows for instance to model reliability of arguments varying over time. We show how E-TAF can be enriched by considering Structured Abstract Argumentation, adding compositional elements to the abstract arguments involved based on a simplified version of the recently introduced Dynamic Argumentation Frameworks.

Fixpoints play a key role in the mathematical set up of abstract argumentation theory but, we argue, have been relatively underexamined in the literature. The paper studies the logical structure underlying the computation via approximation sequences of the sort of fixpoints relevant in argumentation. Concretely, it presents a number of novel results on the fixed point theory underpinning the main Dung's semantics and, inspired by recent literature on the logical analysis of equilibrium computation in games, it provides a characterization of those semantics in terms of iterated model updates.

Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the areas of Knowledge Representation and Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semistable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems.

We introduce matrix and its block to the Dung's theory of argumentation framework. It is showed that each argumentation framework has a matrix representation, and the indirect attack relation and indirect defence relation can be characterized by computing the matrix. This provide a powerful mathematics way to determine the "controversial arguments" in an argumentation framework. Also, we introduce several kinds of blocks based on the matrix, and various prudent semantics of argumentation frameworks can all be determined by computing and comparing the matrices and their blocks which we have defined. In contrast with traditional method of directed graph, the matrix method has an excellent advantage: computability(even can be realized on computer easily). So, there is an intensive perspective to import the theory of matrix to the research of argumentation frameworks and its related areas.