Recently, Argumentation Mechanism Design (ArgMD) was introduced as a new paradigm for studying argumentation among self-interested agents using game-theoretic techniques. Preliminary results showed a condition under which a direct mechanism based on Dung's grounded semantics is strategy-proof (i.e. truth enforcing). But these early results dealt with a highly restricted form of agent preferences, and assumed agents can only hide, but not lie about, arguments. In this paper, we characterise strategy-proofness under grounded semantics for a more realistic preference class (namely, focal arguments). We also provide the first analysis of the case where agents can lie.

Equilibrium or near-equilibrium solutions to very large extensive form games are often computed by using abstractions to reduce the game size. A common abstraction technique for games with a large number of available actions is to restrict the number of legal actions in every state. This method has been used to discover equilibrium solutions for the game of no-limit heads-up Texas Hold'em. When using a solution to an abstracted game to play one side in the un-abstracted (real) game, the real opponent actions may not correspond to actions in the abstracted game. The most popular method for handling this situation is to translate opponent actions in the real game to the closest legal actions in the abstracted game. We show that this approach can result in a very exploitable player and propose an alternative solution. We use probabilistic mapping to translate a real action into a probability distribution over actions, whose weights are determined by a similarity metric. We show that this approach significantly reduces the exploitability when using an abstract solution in the real game.

We propose an approach for automatically ranking structured documents applied to patent prior art search. Our model, SVM Patent Ranking (SVM_PR) incorporates margin constraints that directly capture the specificities of patent citation ranking. Our approach combines patent domain knowledge features with meta-score features from several different general Information Retrieval methods. The training algorithm is an extension of the Pegasos algorithm with performance guarantees, effectively handling hundreds of thousands of patent-pair judgements in a high dimensional feature space. Experiments on a homogeneous essential wireless patent dataset show that SVM_PR performs on average 30%-40% better than many other state-of-the-art general-purpose Information Retrieval methods in terms of the NDCG measure at different cut-off positions.

We present an unified methodology for representation and development of dialectical proof procedures in both abstract and assumption-based argumentation based on the notions of legal environments and dispute derivation. A legal environment specifies the legal moves of the dispute parties while a dispute derivation describes the procedure structure. A key insight of this paper is that the opponent moves determine the soundness of a dispute while its completeness depends on the proponent moves.

This paper discusses the state of the art in CBR ontologies from the perspective of one developing an improved system for case-based legal reasoning. The paper proposes three specific roles for a CBR ontology and illustrates them in the context of the intended output of the new system: a legal classroom discussion of how to decide a case featuring hypothetical reasoning and abstract analogies. The paper distills the ontological requirements for modeling the example’s case-based arguments and assesses whether current research can meet those requirements. The concrete example helps to focus on and define goals for improving CBR ontologies.

We present an approach to patent claim simplification which segments claim sentences into clausal discourse units, transforms them into complete sentences, establishes coreference relations and builds a discourse structure between discourse units. The four stages are necessary to allow for the syntactic analysis of otherwise unparsable claim sentences and their regeneration using discourse structure and coreference relations in order to ensure the production of a cohesive and coherent paraphrase/summary.

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

In a significant minority of cases, certain pronouns, especially the pronoun it, can be used without referring to any specific entity. This phenomenon of pleonastic pronoun usage poses serious problems for systems aiming at even a shallow understanding of natural language texts. In this paper, a novel approach is proposed to identify such uses of it: the extrapositional cases are identified using a series of queries against the web, and the cleft cases are identified using a simple set of syntactic rules. The system is evaluated with four sets of news articles containing 679 extrapositional cases as well as 78 cleft constructs. The identification results are comparable to those obtained by human efforts.

A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and compute bounds on partial assignments asynchronously. The asynchronous bounds computation is based on the propagation of partial assignments. The asynchronous forward-bounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. The algorithm is described in detail and its correctness proven. Experimental evaluation shows that AFB outperforms synchronous branch and bound by many orders of magnitude, and produces a phase transition as the tightness of the problem increases. This is an analogous effect to the phase transition that has been observed when local consistency maintenance is applied to MaxCSPs. The AFB algorithm is further enhanced by the addition of a backjumping mechanism, resulting in the AFB-BJ algorithm. Distributed backjumping is based on accumulated information on bounds of all values and on processing concurrently a queue of candidate goals for the next move back. The AFB-BJ algorithm is compared experimentally to other DisCOP algorithms (ADOPT, DPOP, OptAPO) and is shown to be a very efficient algorithm for DisCOPs.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed. An AI framework has been used to implement an AI module for one of the busiest immigration agencies in the world.