Towards a new approach for integrating multimodal user input based on evolutionary computation

2002 ◽  
Author(s):  
Frank Althoff ◽  
Marc Al-Hames ◽  
Gregor McGlaun ◽  
Manfred Lang
Keyword(s):  
Evolutionary Computation ◽  
New Approach ◽  
User Input

scholarly journals Query Recommendation for Improving Search Engine Results

2011 ◽  
Vol 1 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Hamada M. Zahera ◽  
Gamal F. El-Hady ◽  
W. F. Abd El-Wahed
Keyword(s):  
Search Engine ◽  
User Satisfaction ◽  
Real Data ◽  
Search Process ◽  
Data Sets ◽  
New Approach ◽  
Query Log ◽  
User Input ◽  
Query Recommendation ◽  
Web Contents

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

scholarly journals Modelling other agents through evolutionary behaviours

2021 ◽  
Author(s):  
Yifeng Zeng ◽  
Qiang Ran ◽  
Biyang Ma ◽  
Yinghui Pan
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Computation ◽  
The Other ◽  
Decision Quality ◽  
Genetic Operators ◽  
New Approach ◽  
Artificial Intelligence Research ◽  
New Methods ◽  
The Subject ◽  
First Time

AbstractModelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent’s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents’ behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support.

An Explicit Memory Scheme of Genetic Network Programming

2012 ◽  
Vol 16 (7) ◽  
pp. 851-863
Author(s):  
Shingo Mabu ◽  
◽  
Fengming Ye ◽  
Kotaro Hirasawa
Keyword(s):  
Evolutionary Computation ◽  
Explicit Memory ◽  
Directed Graphs ◽  
Genetic Network ◽  
Evolutionary Strategies ◽  
Network Programming ◽  
New Approach ◽  
Memory Scheme ◽  
Genetic Network Programming ◽  
Gene Information

Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the study of evolutionary computation. And recently, a new approach named Genetic Network Programming (GNP) has been proposed especially for solving complex problems in dynamic environments. It is based on the algorithms of classical evolutionary computation techniques and uses data structures of directed graphs which are the unique feature of GNP. Focusing on GNP’s distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for standard GNP in order to improve the performance of GNP by adopting an explicit memory scheme which records and utilizes the exploited information flexibly and extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated in the memory during evolution. Among the accumulated information, some of them are selected and used to guide the agents. In this paper, the proposed architecture is applied to the tileworld which is an excellent benchmark for evaluating the architecture demonstrating its superiority.

scholarly journals A model for contextual data sharing in smartphone applications

2016 ◽  
Vol 12 (3) ◽  
pp. 310-331 ◽  
Author(s):  
Harshvardhan Jitendra Pandit ◽  
Adrian O’Riordan
Keyword(s):  
Data Sharing ◽  
Contextual Information ◽  
Cloud Services ◽  
Smartphone Apps ◽  
Smartphone Applications ◽  
Contextual Data ◽  
New Approach ◽  
Content Type ◽  
User Input ◽  
Practical Implications

Purpose The purpose of this paper is to introduce a model for identifying, storing and sharing contextual information across smartphone apps that uses the native device services. The authors present the idea of using user input and interaction within an app as contextual information, and how each app can identify and store contextual information. Design/methodology/approach Contexts are modeled as hierarchical objects that can be stored and shared by applications using native mechanisms. A proof-of-concept implementation of the model for the Android platform demonstrates contexts modelled as hierarchical objects stored and shared by applications using native mechanisms. Findings The model was found to be practically viable by implemented sample apps that share context and through a performance analysis of the system. Practical implications The contextual data-sharing model enables the creation of smart apps and services without being tied to any vendor’s cloud services. Originality/value This paper introduces a new approach for sharing context in smartphone applications that does not require cloud services.

scholarly journals Query Recommendation for Improving Search Engine Results

2013 ◽  
pp. 46-53 ◽  
Author(s):  
Hamada M. Zahera ◽  
Gamal F. El-Hady ◽  
W. F. Abd El-Wahed
Keyword(s):  
Search Engine ◽  
User Satisfaction ◽  
Real Data ◽  
Data Sets ◽  
New Approach ◽  
Query Log ◽  
User Input ◽  
Search Results ◽  
Query Recommendation ◽  
Web Contents

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.

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