Satoshi Tojo

FLAP Journal 2026 Journal Article

A Representation of Explicit Knowledge and Epistemic Indistinguishability in a Logic of Awareness

• Yudai Kubono
• Satoshi Tojo

The logic of awareness, first proposed by Fagin and Halpern, addressed the problem of logical omniscience by introducing the notion of awareness and dis- tinguishing explicit knowledge from implicit knowledge. In their framework, explicit knowledge was defined as the conjunction of implicit knowledge and awareness, each of which was represented by modal operators. Their definition, however, may derive undesirable propositions that cannot be considered explicit knowledge when Modus Ponens is applied within implicit knowledge. Hence, fo- cusing on indistinguishability among possible worlds, dependent on awareness, we refine the definition of explicit knowledge. In our semantics, we require that the aware implicit knowledge is not necessarily explicit knowledge, though ex- plicit knowledge must be aware as well as implicit. We employ an example of elementary geometry, where different students may or may not reach the final answer, depending on whether they are aware of learned mathematical facts. Thereafter, we formally present the syntax and the semantics of our language, named Awareness-Based Indistinguishability Logic (AIL). We prove that AIL has more expressive power than the logic of Fagin and Halpern, and show that the latter is embeddable in AIL. Furthermore, we provide an axiomatic system of AIL and prove its soundness and completeness.

LORI Conference 2025 Conference Paper

Intentionally Anonymous Public Announcements

• Thomas Ågotnes
• Rustam Galimullin
• Ken Satoh
• Satoshi Tojo

Abstract We formalise the notion of an intentionally anonymous public announcement in the tradition of public announcement logic. An anonymous announcement can be seen as in-between a public announcement from “the outside” (an announcement of \(\varphi \) ) and a public announcement by one of the agents a (an announcement of \(K_a\varphi \) ): we get more information than just \(\varphi \), but not (necessarily) about exactly who made it. In this paper we assume that it is common knowledge that the announcer intended to be anonymous. Like in the Russian Cards puzzle, with that assumption, anonymous announcements in fact reveal more information than without. We introduce an operator for intentionally anonymous announcements, and show that in several ways it all boils down to the notion of a “safe” announcement (again, similarly to Russian Cards). We model safety via a fixed-point operator that is similar to common knowledge. Main formal results include comparisons of expressivity and axiomatic completeness for a language expressing safety.

AILAW Journal 2020 Journal Article

Encoded summarization: summarizing documents into continuous vector space for legal case retrieval

• Vu Tran
• Minh Le Nguyen
• Satoshi Tojo
• Ken Satoh

Abstract We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65. 6% and 57. 6% on the experimental datasets of legal case retrieval tasks.

AILAW Journal 2018 Journal Article

Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts

• Truong-Son Nguyen
• Le-Minh Nguyen
• Satoshi Tojo
• Ken Satoh
• Akira Shimazu

Abstract This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation (RE) parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF models and the unified model approach with the multilayer BiLSTM-CRF model and the multilayer BiLSTM-MLP-CRF model. Experimental results on two Japan law RRE datasets demonstrated advantages of our proposed models. For the Japanese National Pension Law dataset, our approaches obtained an \(F_{1}\) score of 93. 27% and exhibited a significant improvement compared to previous approaches. For the Japan Civil Code RRE dataset which is written in English, our approaches produced an \(F_{1}\) score of 78. 24% in recognizing RE parts that exhibited a significant improvement over strong baselines. In addition, using external features and in-domain pre-trained word embeddings also improved the performance of RRE systems.

AILAW Journal 2017 Journal Article

Dynamic epistemic logic of belief change in legal judgments

• Pimolluck Jirakunkanok
• Katsuhiko Sano
• Satoshi Tojo

Abstract This study realizes belief/reliability change of a judge in a legal judgment by dynamic epistemic logic (DEL). A key feature of DEL is that possibilities in an agent’s belief can be represented by a Kripke model. This study addresses two difficulties in applying DEL to a legal case. First, since there are several methods for constructing a Kripke model, our question is how we can construct the model from a legal case. Second, since this study employs several dynamic operators, our question is how we can decide which operators are to be applied for belief/reliability change of a judge. In order to solve these difficulties, we have implemented a computer system which provides two functions. First, the system can generate a Kripke model from a legal case. Second, the system provides an inconsistency solving algorithm which can automatically perform several operations in order to reduce the effort needed to decide which operators are to be applied. By our implementation, the above questions can be adequately solved. With our analysis method, six legal cases are analyzed to demonstrate our implementation.

FLAP Journal 2017 Journal Article

Teaching Modal Logic from the Linear Algebraic Viewpoint.

• Ryo Hatano
• Katsuhiko Sano
• Satoshi Tojo

This paper proposes a linear algebraic approach to teach modal logic to stu- dents who might not be familiar with first-order logic. Our approach is based on Fitting’s linear algebraic reformulation of Kripke semantics of modal logic. A key idea of his reformulation is to represent an accessibility relation R by a square matrix and a valuation V (p) of an atomic variable p by a column vector. Then, we may calculate the truth set of 3p as the multiplication of the square matrix R for the accessibility relation and the column vector for p. Hence, we can regard such matrix calculation as an extended version of truth table calcu- lation. We discuss how our reformulation is useful to teach modal logic to our target students before teaching first-order logic. In addition, we present our supporting software to avoid involved calculations on matrices and explain how we can use it for educational purposes.

LPAR Conference 2015 Conference Paper

A Labelled Sequent Calculus for Intuitionistic Public Announcement Logic

• Shoshin Nomura
• Katsuhiko Sano
• Satoshi Tojo

Abstract Intuitionistic Public Announcement Logic (IntPAL) proposed by Ma et al. (2014) aims at formalizing changes of an agent’s knowledge in a constructive manner. IntPAL can be regarded as an intuitionistic generalization of Public Announcement Logic (PAL) whose modal basis is the intuitionistic modal logic IK by Fischer Servi (1984) and Simpson (1994). We also refer to IK for the basis of this paper. Meanwhile, Nomura et al. (2015) provided a cut-free labelled sequent calculus based on the study of Maffezioli et al. (2010). In this paper, we introduce a labelled sequent calculus for IntPAL (we call it \(\mathbf {GIntPAL}\) ) as both an intuitionistic variant of \(\mathbf {GPAL}\) and a public announcement extension of Simpson’s labelled calculus, and show that all theorems of the Hilbert axiomatization of IntPAL are also derivable in \(\mathbf {GIntPAL}\) with the cut rule. Then we prove the admissibility of the cut rule in \(\mathbf {GIntPAL}\) and also the soundness result for birelational Kripke semantics. Finally, we derive the semantic completeness of \(\mathbf {GIntPAL}\) as a corollary of these theorems.

I&C Journal 2011 Journal Article

Efficiency of the symmetry bias in grammar acquisition

• Ryuichi Matoba
• Makoto Nakamura
• Satoshi Tojo

It is well known that the symmetry bias greatly accelerates vocabulary learning. In particular, the bias helps infants to connect objects with their names easily. However, grammar learning is another important aspect of language acquisition. In this study, we propose that the symmetry bias also helps to acquire grammar rules faster. We employ the Iterated Learning Model, and revise it to include the symmetry bias. The result of the simulations shows that infants could abduce the meanings from unrecognized utterances using the symmetry bias, and acquire compositional grammar from a reduced amount of learning data.

AILAW Journal 1997 Journal Article

Similarity of Legal Cases: From Temporal Relations of Affairs

• Satoshi Tojo
• Katsumi Nitta

Case-based reasoning has played an important role in legal reasoning systems. As one criteria for similarity of cases, temporal relationsamong affairs in legal cases should be compared. Thus far in many legalreasoning systems, cases have been described as sequences of pointwiseevents, or at best, simple time intervals, and they have been related bypredicates such as before, after, while, and so on. However, such relations may depend on each implementer'spersonal view, and also require much labor to write down by hand. In this paper, we first propose a classification of affair types by their temporal features, and according to those types, we propose several assumption rules that prescribe the temporal relations between affair types. The temporal relations are automatically generated by these rules. Thereafter, we discuss how thesetemporal relations work in the comparison of similarity of cases. Inthe process of comparison, inadequate temporal relations need to beamended. For this purpose, we introduce revision rules, that refute theresults of assumption rules.

ECAI Conference 1992 Conference Paper

Intuitive Classification Based on Affinity

• Yasuhiko Saito
• Satoshi Tojo
• Seiichi Komiya