Katsumi Nitta

Computing Argumentation Semantics in Answer Set Programming

We propose a simple and generic method for computing Dungs standard argumentation semantics along with semi-stable semantics in Answer Set Programming (ASP). The different semantics captured by argumentation frameworks are all uniformly represented in our ASP setting. It is based on Caminadas reinstatement labellings for argumentation frameworks as well as our method of computing circumscription in ASP. In our approach, a given argumentation framework is translated into a single normal logic program w.r.t. the chosen semantics whose answer set (if exists) yields an argument-based extension expressed by means of a reinstatement labelling for the semantics. We show soundness and completeness theorems for our translation, which allow us not only to compute argument-based extensions but also to decide whether an argument is sceptically or credulously accepted w.r.t. the chosen semantics. Based on our theorems, the prototype argumentation system was implemented using the ASP solver, DLV, whose evaluation results verified correctness of our approach.

New HELIC-II: a software tool for legal reasoning

The new IL%?ZC-11 is a software tool for legal reasoning. It consists of two functions argumentation function and debating function. Argumentation function is retilzed by a typed logic programming language with generalization of rules and defea.sible reasoning based on priority of rules. Debating function is realized by meta knowledge which controls the argumentation function. This paper introduces overview of the new HELICH system. We show how legal knowledge is represented in the new HELIC-11 illustrated by presenting the example of an actual criminzd case.

A computational model for trial reasoning

The purpose of this paper is to describe a computational model for legal reasoning in criminal law (i.e. trial reasoning). This logic-programming based model contains seven key components: facts of a new case, old cases, domain knowledge, meta rules, similarity matching relations, various implications, and two explicit agents, the plaintiff and the defendant, with opposing goals and reasoning strategies. The argumentation process in this model can be likened to a two-agent game. One agent puts forward an argument. The other agent recognizes the situation, generates candidates to refute the claim, and selects the best one for the next move. The game ends when any one agent can no longer make a move. Certain debate strategies of this model are illustrated in this paper with examples. In addition, the computational model presented has been used in the design and development of HELIC-II - a parallel knowledge-based system for trial reasoning.

Computing Preferred Answer Sets in Answer Set Programming

Prioritized logic programs (PLPs) have a mechanism of representing priority knowledge in logic programs. The declarative semantics of a PLP is given as preferred answer sets which are used for representing nonmonotonic reasoning as well as preference abduction. From the computational viewpoint, however, its implementation issues have little been studied and no sound procedure is known for computing preferred answer sets of PLPs. In this paper, we present a sound and complete procedure to compute all preferred answer sets of a PLP in answer set programming. The procedure is based on a program transformation from a PLP to a logic program and is realized on top of any procedure for answer set programming. The proposed technique also extends PLPs to handle dynamic preference and we address its application to legal reasoning.

HELIC-II: legal reasoning system on the parallel inference machine

This paper presents HELIC-II, a legal reasoning system on the parallel inference machine. HELIC-II draws legal conclusions for a given case by referring to a statutory law (legal rules) and judicial precedents (old cases). This system consists of two inference engines. The rule-based engine draws legal consequences logically by using legal rules. The case-based engine generates legal concepts by referencing similar old cases. These engines complementally draw all possible conclusions, and output them in the form of inference trees. Users can use these trees as material to construct arguments in a legal suit.HELIC-II is implemented on the parallel inference machine, and it can draw conclusions quickly by parallel inference.As an example, a legal inference system for the Penal Code is introduced, and the effectiveness of the legal reasoning and parallel inference model is shown.

Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets

This paper focuses on the computational analysis of the individual communication skills of participants in a group. The computational analysis was conducted using three novel aspects to tackle the problem. First, we extracted features from dialogue (dialog) act labels capturing how each participant communicates with the others. Second, the communication skills of each participant were assessed by 21 external raters with experience in human resource management to obtain reliable skill scores for each of the participants. Third, we used the MATRICS corpus, which includes three types of group discussion datasets to analyze the influence of situational variability regarding to the discussion types. We developed a regression model to infer the score for communication skill using multimodal features including linguistic and nonverbal features: prosodic, speaking turn, and head activity. The experimental results show that the multimodal fusing model with feature selection achieved the best accuracy, 0.74 in R2 of the communication skill. A feature analysis of the models revealed the task-dependent and task-independent features to contribute to the prediction performance.

Context-based conversational hand gesture classification in narrative interaction

Communicative hand gestures play important roles in face-to-face conversations. These gestures are arbitrarily used depending on an individual; even when two speakers narrate the same story, they do not always use the same hand gesture (movement, position, and motion trajectory) to describe the same scene. In this paper, we propose a framework for the classification of communicative gestures in small group interactions. We focus on how many times the hands are held in a gesture and how long a speaker continues a hand stroke, instead of observing hand positions and hand motion trajectories. In addition, to model communicative gesture patterns, we use nonverbal features of participants addressed from participant gestures. In this research, we extract features of gesture phases defined by Kendon (2004) and co-occurring nonverbal patterns with gestures, i.e., utterance, head gesture, and head direction of each participant, by using pattern recognition techniques. In the experiments, we collect eight group narrative interaction datasets to evaluate the classification performance. The experimental results show that gesture phase features and nonverbal features of other participants improves the performance to discriminate communicative gestures that are used in narrative speeches and other gestures from 4% to 16%.

Negotiation strategy of agents in the MONOPOLY game

In this paper, we discuss negotiation strategy of agents that play the board game MONOPOLY. First, we developed MONOPOLY server for the purpose of making agents confront on network and a MONOPOLY agent which negotiates with the other agents. Its negotiation strategy is based on a game theory using an evaluation function. This function includes pattern of color groups, money and position of tokens. The agent compares the proposal candidates for trading by using this function and selects the best one. Finally, the experimental results show that this agent system can be benchmark for studying negotiation strategy.

A tool for animated agents in network-based negotiation

In this paper, we describe a tool for developing animated agents with facial expressions in negotiation through a computer network. The tool learns a users tendency to select facial expressions of the animated agent, and generates facial expressions instead of human. In order to estimate facial expressions, the tool has an emotional model constructed by Bayesian network. We can easily develop animated agents if we use this tool as a component. And we describe the estimation of an opponents emotional state, based on observed data, by using the Bayesian network.

Protein Sequence Analysis by Parallel Inference Machine

Institute for New Generation Computer Technology (ICOT) is the research center of the Fifth Generation Computer Project in Japan. ICOT has developed and applied its knowledge processing technologies based on logic programming into such things as a parallel computer, PIM; a database management system, Kappa; a deductive and object oriented database language, Quixote; and various knowledge processing tools. In order to evaluate the effectiveness of our technologies, we developed genome analysis software such as protein sequence analysis programs, a protein folding simulator and an integrated protein knowledge base.