5th International Conference on Artificial Intelligence and Soft Computing (AISO 2019)

December 14~15, 2019, Chennai, India

Accepted Papers

Precedent Case Retrieval Using Wordnet and Deep Recurrent Neural Networks
Sai Vishwas Padigi, Mohit Mayank and Prof. S. Natarajan, Department of Computer Science and Engineering, PES University, Bengaluru, India
The slowness of legal proceedings in the common law legal system is a widely known fact. Any tool which could help reduce the time taken for the resolution of a case is invaluable. Common legal systems place a great importance on precedents and retrieving the correct set of precedents is considerably time consuming. Hence, for any case whose proceedings are in progress, if there are suitable prior cases, then the court has to follow the same interpretations that were passed in the prior cases. This is to ensure that similar situations receive similar treatment, thus maintaining uniformity amongst the legal proceedings across all courts at all times. Hence, precedent cases are treated as important as any other written law (a statute) in this legal system. In this paper, we propose two new approaches to solve this information retrieval problem wherein the system accepts the current case document as the query and returns the relevant precedent cases as the result. The first approach is to calculate the document similarity using Wordnet, which is a lexical database that could be leveraged to quantify the semantic relatedness between two documents, using a semantic network. The second approach is the use of a Siamese Manhattan Long Short Term Memory network, which is a supervised model trained to understand the underlying similarity between two documents.

Information retrieval, Text similarity, Deep learning, Legal documents, Wordnet, Siamese Manhattan LSTM

Encoding Graph Transformation Systems With Symmetric Nets
Lorenzo Capra, Universit`a degli Studi di Milano, Milan, Italy
Graph transformation systems (GTS) have been successfully proposed as a general, theoretically sound model for concurrency. Petri nets (PN), on the other side, are a central and intuitive formalism for concurrent or distributed systems, well supported by a number of analysis techniques/tools. Some PN classes have been shown to be instances of GTS. In this paper, we change perspective presenting an operational semantics of GTS in terms of Symmetric Nets, a well-known class of Coloured Petri nets featuring a structured syntax that outlines model symmetries. Some practical exploitations of the proposed operational semantics are discussed. In particular, a recently developed structural calculus for SN is used to validate graph rewriting rules in a symbolic way.

Formal models, Graph Transformation Systems, (High-Level) Petri Nets.

Test for Uniform Bounded Input, Bounded Output Stability
Rama Murthy and Mahipal Jetta, Mahindra Ecole Centrale, Hyderabad, India
In this research paper, a novel concept of Uniform Bounded InputBounded Output (BIBO) stability is introduced. Using the state space formulation of a Linear Time Invariant (LTI) system, a test for UNIFORM BIBO stability is designed. It is expected that this test will be useful in voltage stabilizers and other control equipment.

BIBO Stability, Poles, Eigen Values, Relative Stability, Linear System