Hiroyuki Kido

Data Jackets for Synthesizing Values in the Market of Data

Abstract In order to make a social environment where analysts and decision makers in active businesses and sciences can be provided with data they need, we discuss why we should and how we can (re)design an environment called the market of data, where each user or provider of data can externalize and share the value of each part of data so that one can buy/sell it in a reasonable condition, e.g., for a reasonable price or as open source if it may give merits to people in general rather than to particular segments who can pay. Presenting one way to go, that is called Innovators’ Marketplace on Data Jackets, we show a vision for the systematic design of the market of data where existing tools and new technologies are configured, such as the visualization of the relation among databases/datasets for aiding stakeholders’ communication about possible use scenarios of data. By this, this paper calls for potential contributors in the future to the market of data.

Data Jackets for Externalizing Use Value of Hidden Datasets

Abstract Data Jackets, that are small pieces of information containing the abstracts of data that exist but cannot be disclosed, are encouraged to be submitted to the market, by showing a result of the experimental process with Innovators Marketplace on Data Jackets (IMDJ). The process started from participants’ submission of data jackets, on which they then played a creative game where they proposed ideas, to combine DJs and analyze the obtained dataset, and evaluate the expected knowledge to be obtained by each others idea. Finally we had other group of people including those who did not participate in the game, and had them propose stakeholders who may give more information about the data corresponding to the data jackets. As a result, the conceived value of the data corresponded between in and after the game.

Justifying Underlying Desires for Argument-Based Reconciliation

Not focusing on stakeholders’ original desires, but on their underlying desires helps agents to reconcile practical conflicts. This paper proposes a logical formalization of an argument-based reasoning for justifying both underlying desires and means for realizing them. Based on the idea that an underlying desire can be obtained by abstracting an original desire, we give a problem setting for desire abstraction in terms of sufficiency and consistency using practical syllogisms. We introduce two kinds of defeasible inference rules, called positive and negative practical abductive syllogisms, as counterparts of the practical syllogisms and show their correctness in terms of sufficiency and consistency. We give three kinds of argumentation systems structured with practical abductive syllogisms or/and practical syllogisms and show that the argumentation systems can simply handle Kowalski and Toni’s reconciliatory scenario for committee member selection and our reconciliatory scenario for business transfer.

Developing SNS Tool for Consensus Building on Environmental Safety Using Assurance Cases

Systems have been connected and interacted with each other around our daily lives. The boundaries of the systems are no more exist, and the safety of the systems involves various stakeholders including professionals, governments, and ordinary citizens. Therefore, for the safety of systems and the environments, consensus building among various stakeholders (e.g., professionals, developers, government, citizens) is crucial. However, ordinary citizens usually does not have sufficient knowledge about the safety and risk of systems around them. To solve this problem, we aim to develop methods and tools for consensus building specially with citizens using assurance cases written in GSN. This paper specifies the initial study for the goal. We take radiation information as an example. We implement prototype tools for visualizing structured argument by GSN about radiation information for citizens, and conduct an experiment for the effectiveness of the tool. The preliminary result indicates that the tool based on GSN is statistically significantly effective for sharing correct radiation information with citizens.

Learning Argument Acceptability from Abstract Argumentation Frameworks

This paper introduces argument-based decision-tree for learning acceptability of arguments. We specifically examine an attack relation existing between arguments, without referring to any contents, either sentences or words, existing in individual arguments. This idea is formalized using decision trees in which their attributes are instantiated by complete, preferred, stable and grounded extensions, respectively, defined by acceptability semantics. This study extracted 38 arguments and 4 utterers from an argument about euthanasia that actually took place on a social media site. Also, 21 training data were collected by asking them to express their attitudes either for or against the individual 38 arguments. By stratifying audiences in accordance with consistency with utterers, leave-two-out cross validation yielded results with a 0.73 AUC value, on average. This fact demonstrates that our argument-based decision-tree learning is expected to be fairly useful for agents who have a definite position on an issue of argument.

Shift from Forward to Backward Deliberation in Search of Reconciliation

Desire conflicts arise in several real-world contexts. In this paper we propose a mixed deliberation dialogue for reconciliation. A mixed deliberation dialogue is defined as a combination of forward and backward deliberation dialogues whose goals are subordinate and superordinate desires of a given desire, respectively. This research and the introduction of mixed deliberation dialogue have been motivated by Kowalski and Toni’s reconciliatory scenario: indeed we show that an instantiation of a mixed deliberation dialogue implements key parts of Kowalski and Toni’s reconciliatory solution. We also proved the correctness of the mixed deliberation dialogues.

A Supplemental Notation of GSN Aiming for Dealing with Changes of Assurance Cases

An assurance case is a document containing arguments about risk-related issues on a system and regarded as an effective means to achieve open systems dependability. This paper proposes a notation of an assurance case to deal with changes of a system which can pose challenges to an established assurance case. The proposed notation is based on GSN and the presented case study suggests that the notation can make change-management of assurance cases easier. We also show that a GSN with a confidence map can be expressed by the proposed notation.

Defensibility-Based Classification for Argument Mining

This paper shows a preliminary report regarding classification techniques based on argumentation theory in artificial intelligence. A classification problem is defined on a directed graph, i.e., An argumentation framework, where each node represents an argument and each edge an attack relation between connected arguments. A hypothesis space is defined by all possible argumentation consequences, i.e., Extensions. A target argument is classified as justified or overruled, according to the best extensions minimizing errors with respect to training examples, i.e., Tuples of arguments and their correct classes. We give ideal downward and upward refinement operators for calculating hypotheses step by step. Algorithm analysis and performance evaluation are future work.

Restructuring Incomplete Models in Innovators Marketplace on Data Jackets

Innovators Marketplace, a market-like workshop where cards showing existing pieces of knowledge in various domains are combined to create ideas of services/products and thrown into demand-driven communication to choose practical ideas, has been extended to a setting of the market of data. This extension is called Innovators Marketplace on Data Jackets, a workshop in which each prepared card called a data jacket represents the digest knowledge about a dataset, that is, a kind of metadata. Data jackets are disclosed, whereas the corresponding data are not, and participants of the workshop create ideas for combining and analyzing data using the visualized correlation of data jackets. In this chapter, this workshop is described as a systematic process for reasoning on incomplete models , where each data jacket is regarded as an incomplete local model in the domain of the data, and communication is launched for satisfying requirements in the market (regarded as incomplete global models) by restructuring and combining local models. The data jacket may initially include atoms and terms in the domain, not connected via complete causal relations. Via the communication, however, links including causal relations appear and are revised toward obtaining a glocal model corresponding to a solution to satisfy requirements in the marketplace. In this process, the local model corresponding to each element is also revised to obtain useful knowledge digesting the corresponding data.

Paretian argumentation frameworks for Pareto optimal arguments

Argument-based reasoning offers promising interaction and computation mechanisms for multi-agent negotiation and deliberation. Arguments in this context are typically statements of beliefs or actions related to agents’ subjective values, preferences, and so on. Consequences of such arguments can and should be evaluated using various criteria, and therefore, it is desirable that semantics supports these criteria as principles for accepting arguments. This paper gives an instance of Dung’s abstract argumentation framework to deal with Pareto optimality, i.e., a fundamental criterion for social welfare. We show that the instance allows Dung’s acceptability semantics to interpret Pareto optimal arguments, without loss of generality. We discuss the prospects of justified Pareto optimal arguments and Pareto optimal extensions.