A Recognition Approach for Adversarial Planning Based on Complete Goal Graph

2007 ◽  
Author(s):  
Ji-Li Yin ◽  
Ying Liu ◽  
Jin-Yan Wang ◽  
Wen-Xiang Gu ◽  
Yin-Ping Zhang
Keyword(s):  
Goal Graph

scholarly journals Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 134 ◽  
Author(s):  
Noriyuki Kushiro ◽  
Ami Fukuda ◽  
Masatada Kawatsu ◽  
Toshihiro Mega
Keyword(s):  
Linear Regression ◽  
Energy Demand ◽  
Mixture Modeling ◽  
Data Partitioning ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Heterogeneous Mixture ◽  
Energy Prediction ◽  
Consumption Data ◽  
Goal Graph

In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets.

scholarly journals Requirements Elicitation with Extended Goal Graph

2016 ◽  
Vol 96 ◽  
pp. 1691-1700 ◽  
Author(s):  
Noriyuki Kushiro ◽  
Takuro Shimizu ◽  
Tatsuya Ehira
Keyword(s):  
Requirements Elicitation ◽  
Goal Graph

scholarly journals Impact Analysis on an Attributed Goal Graph

2012 ◽  
Vol E95-D (4) ◽  
pp. 1012-1020 ◽  
Author(s):  
Shinpei HAYASHI ◽  
Daisuke TANABE ◽  
Haruhiko KAIYA ◽  
Motoshi SAEKI
Keyword(s):  
Impact Analysis ◽  
Goal Graph

scholarly journals Goal Recognition through Goal Graph Analysis

2001 ◽  
Vol 15 ◽  
pp. 1-30 ◽  
Author(s):  
J. Hong
Keyword(s):  
Graph Analysis ◽  
Polynomial Space ◽  
Graph Structure ◽  
Excellent Performance ◽  
Time Step ◽  
Novel Approach ◽  
Consecutive Time ◽  
Goal Recognition ◽  
Exponential Size ◽  
Goal Graph

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

Sign in / Sign up

Export Citation Format

Share Document