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.

We introduce matrix and its block to the Dung's theory of argumentation frameworks. It is showed that each argumentation framework has a matrix representation, and the common extension-based semantics of argumentation framework can be characterized by blocks of matrix and their relations. In contrast with traditional method of directed graph, the matrix way has the advantage of computability. Therefore, it has an extensive perspective to bring the theory of matrix into the research of argumentation frameworks and related areas.

Dung's famous abstract argumentation frameworks represent the core formalism for many problems and applications in the field of argumentation which significantly evolved within the last decade. Recent work in the field has thus focused on implementations for these frameworks, whereby one of the main approaches is to use Answer-Set Programming (ASP). While some of the argumentation semantics can be nicely expressed within the ASP language, others required rather cumbersome encoding techniques. Recent advances in ASP systems, in particular, the metasp optimization frontend for the ASP-package gringo/claspD provides direct commands to filter answer sets satisfying certain subset-minimality (or -maximality) constraints. This allows for much simpler encodings compared to the ones in standard ASP language. In this paper, we experimentally compare the original encodings (for the argumentation semantics based on preferred, semi-stable, and respectively, stage extensions) with new metasp encodings. Moreover, we provide novel encodings for the recently introduced resolution-based grounded semantics. Our experimental results indicate that the metasp approach works well in those cases where the complexity of the encoded problem is adequately mirrored within the metasp approach.

The aim of this paper is to announce the release of a novel system for abstract argumentation which is based on decomposition and dynamic programming. We provide first experimental evaluations to show the feasibility of this approach.