Kashif Zafar

Mine Detection and Route Planning in Military Warfare using Multi Agent System

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration

Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developers task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.

Optimization of Route Planning using Simulated Ant Agent System

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation.

Optimization of route planning and exploration using multi agent system

This research presents an optimization technique for route planning and exploration in unknown environments. It employs the hybrid architecture that implements detection, avoidance and planning using autonomous agents with coordination capabilities. When these agents work for a common objective, they require a robust information interchange module for coordination. They cannot achieve the goal when working independently. The coordination module enhances their performance and efficiency. The multi agent systems can be employed for searching items in unknown environments. The searching of unexploded ordinance such as the land mines is an important application where multi agent systems can be best employed. The hybrid architecture incorporates learning real time A* algorithm for route planning and compares it with A* searching algorithm. Learning real time A* shows better results for multi agent environment and proved to be efficient and robust algorithm. A simulated ant agent system is presented for route planning and optimization and proved to be efficient and robust for large and complex environments.

Collaborative Evolutionary Planning Framework (EPF) for Route Planning

research presents a collaborative evolutionary planning framework for large scale grid exploration and planning problems. It caters for both dynamic and unknown environments using evolutionary techniques. In addition, we integrate the exploration and planning process in a unified framework using multi agent system. As a proof of success, we have developed extensive simulation with realistic obstacles and target. Our algorithm addresses the issues involved during such exploration and post exploration route planning. It acts as a controller and navigator for multiple agents and demonstrates the applicability for two different domains, Field Exploration and Route Planning. The EPF uses an optimized search algorithm for exploration phase and genetic algorithm for optimization of route in dynamic environments. The EPF can be used in different exploration and route planning problems but this paper focuses on obstacle detection and avoidance for its implementation.

Voice Controlled Cellular Communication (V3C) System for Special Citizens

ABSTRACT In this paper, we describe the formatting guidelines for IJCA Journal Submission. This paper presents a system for communication and control by disabled people based on automatic recognition of phonemes. This system allows users to navigate around an alphabet board by making phonemic utterances, thus enabling the user to spell out messages. Phoneme recognition provides an alternative to speech recognition technologies for people who have lost the ability to speak but remain capable of producing simple repeatable utterances. Voice Controlled Cellular Communication (V3C) aims at developing a voice-controlled tool for operating computer targeting physically handicapped and blind users having difficulties using a standard keyboard and mouse. It presents an interface that allows a user to activate any web page element through visual enumeration (Indexing) by an appropriate command. General Terms Computational Linguistics, Speech Recognition, Machine Learning et. al.

Feature selection based classification of sentiment analysis using Biogeography optimization algorithm

Sentiment classification of social media has recently become popular among scientists due to the emergence of product reviews, blogs and social networking sites. A large number of reviews are difficult to evaluate personally. Moreover due to variable nature of reviews it becomes difficult, to compile overall result of reviews, to know which product is better than other. Researchers have already implemented machine learning techniques to analyze sentiment present in the given document. But execution time for these techniques increases due to the increase in feature set of data. Also irrelevant features participate in determining the sentiment of the given document, thereby varying the accuracy of the algorithm. In order to get much better classification, we propose a Biogeography based optimization algorithm to select optimal features set from given data. Then by using Naïve Bayes and Support Vector Machine techniques, we perform sentiment classification of product reviews. The proposed technique can be applied to other classification problems where feature set is large.

ROUTE PLANNING AND OPTIMIZATION OF ROUTE USING SIMULATED ANT AGENT SYSTEM

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

Niching with Sub-swarm Based Particle Swarm Optimization

In this study we present a sub-swarm based particle swarm optimization algorithm for niching (NSPSO). The NSPSO algorithm is capable of locating and maintaining a sufficient number of niches throughout the execution of the algorithm. The niches which are identified are then exploited by using a sub-swarm strategy which tries to refine the niche and converge to an optimum solution. NSPSO is capable of locating multiple solutions and is well suited for multimodal optimization problems. From the experimentation results, we have observed that NSPSO is quite efficient in locating both global and local optima. We present a comparison of the performance of NSPSO with NichePSO and SPSO.

Multi Agent Based Mine Detection and Route Planning Using Learning Real Time A*Algorithm

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration