A REACTIVE ARCHITECTURE FOR AUTONOMOUS VIRTUAL AGENTS USING FUZZY LOGIC

ONE OF THE FUNDAMENTAL ASPECTS OF A VIRTUAL ENVIRONMENT IS THE VIRTUAL AGENTS THAT INHABIT THEM. IN MANY APPLICATIONS, VIRTUAL AGENTS ARE REQUIRED TO PERCEIVE INPUT INFORMATION FROM THEIR ENVIRONMENT AND MAKE DECISIONS APPROPRIATE TO THEIR TASK BASED ON THEIR PROGRAMMED REACTION TO THOSE INPUTS;. THE RESEARCH PRESENTED IN THIS THESIS FOCUSES ON THE REACTIVE BEHAVIOUR OF THE AGENTS. WE PROPOSE A NEW CONTROL ARCHITECTURE TO ALLOW AGENTS TO BEHAVE AUTONOMOUSLY IN NAVIGATION TASKS IN UNKNOWN ENVIRONMENTS

A set of rules for constructing gender-based personality types’ composition for software programmer

The current era has been declared as technological era where both profit and no-profit organisations rely solely on software to cope with myriad issues they typically face.The growing demand for software has equally placed challenging tasks on workplaces to produce quality and reliable software.Unfortunately, software development industries have drastically failed to produce software in due time or even if software is produced in time but it fails to yield the desired results.Keeping this problem in view, this study tried to address this problem by offering team composition model lucrative for software development. For instance, Personality types, especially Introvert (I) and Extrovert (E) traits, of team members of software development are explored with gender diversity with a key focus on the programmer role.Moreover, descriptive and predictive approaches were applied to gain the hidden facts from data.The data of this study was taken from both academia and industry to establish the generalizability in the findings.Additionally, different personality traits composition was set based on gender which was not studied in previous studies.The findings of this research suggest that male-programmer should be composed of E trait of personality and, whereas female-programmer should be I.The overall findings contribute to serve the cause of software development team and also contribute to the existing literature on software development and its team composition

Making programmer suitable for team-leader: Software team composition based on personality types

The profuse use of software has turned the world into global village where everything is accessible at finger tips.The past studies have confirmed the rapid increase in the demand of software whereas its quality supply has drastically decreased to 6%. As high demand and low supply normally generate numerous problems, many researchers, therefore, have raised their concern to develop software affordable, less time consuming and feasible to achieve organisational ends. The findings of the past research studies have determined the fact that besides technical skills, human resources (i.e., personality type for team composition) is of pivotal importance for developing software which has not been seriously addressed.This study has tried to address this prevailing problem by focusing on patterns of personality types of programmer role monitored with team-leader. Additionally, to draw the attention of practitioners, the results are validated with several classification techniques and results appeared with high accuracy.The study has implications on both software developers and researchers having their interest in role of team composition in software development

Balancing the personality of programmer: software development team composition

The production of software and their effectiveness have become the prerequisite for the development of various sectors of the world.Persistent demand for the software, feasible and effective in nature to address the clients’ demand have levitated the interest amongst researchers to determine the factors that idealize the software development team since an adept and compatible team members, in terms of personality, are likely to ensure the success of software.In this regard, personality clashes have been attributed as the prominent factors of all to the failure of the software. Although copious research studies have been carried out in the past to suggest ideal and compatible personalities for making an ideal software development team, it is regret to add that the findings of these studies have rather enhanced the gravity of the problem for giving different suggestions for composing an ideal team for software development.To lessen such confusion, this study aims to propose solution for personality-based team composition by executing the different ranges of the programmer’s role based on Myer Brig Type Indicator (MBTI) pairs. This method supposedly allows the researchers to reach the suitable conclusion by thorough investigation of all traits of personality for programmer role.In order to attain the best solution, student population was involved to develop the software projects in teams.The experiments were divided into two segments: defining balancing benchmark and validating the benchmark. In outcomes, this study proposed different ranges of personality traits based on gender classification for software programmers

Software development team composition: Personality types of programmer and complex network

Several authors have identified the different personality types for software team composition. Effective personality types for software development roles is still a question.This study aims to measure the relationship between different personality types by using complex network approach for finding effective nodes of personality type for software programmer.In order to achieve the objective, the study was conducted on student population. Myer-Briggs Type Indicator (MBTI) personality assessment tool was used to obtain the personality types of participants. Furthermore, degree centrality, betweenness centrality, and closeness centrality measures were used on data. These measures were used to find the strongly liked personality types among team members, personality types that can create effective communication, and personality types which can work close with other personality types. Basically, two types of results were obtained from applied measures: personality types which are weighted and frequent and personality types which are weak and less frequent.For example, ISTJ, INFJ, ISTP, and INFP personality types were found very less lucrative in working close with other personality types.On the other hand, ISTJ has been found very effective personality type for programmer role in software development literature. The results suggest that each personality type has its own complex behavior which should be extracted for better outcomes. Deciding one particular personality type for programmer role would be an injustice with it. Therefore, this paper recommends to use complex network phenomenon to extract the hidden facts behind each personality types for software development roles

Reactive behaviour for autonomous virtual agents using fuzzy logic

One of the fundamental aspects of a virtual environment is the virtual agents that inhabit them. In many applications, virtual agents are required to perceive input information from their environment and make decisions appropriate to their task based on their programmed reaction to those inputs. The research presented in this thesis focuses on the reactive behaviour of the agents. We propose a new control architecture to allow agents to behave autonomously in navigation tasks in unknown environments. Our behaviour-based architecture uses fuzzy logic to solve problems of agent control and action selection and which can coordinate conflicts among different operations of reactive behaviours. A Fuzzy Associative Memory (FAM) is used as the process of encoding and mapping the input fuzzy sets to the output fuzzy set and to optimise the fuzzy rules. Our action selection algorithm is based on the fuzzy α-level method with the Hurwicz criterion. The main objective of the thesis was to implement agent navigation from point to point by a coordination of planning, sensing and control. However, we believe that the reactive architecture emerging from this research is sufficiently general that it could be applied to many applications in widely differing domains where real-time decision making under uncertainty is required. To illustrate this generality, we show how the architecture is applied to a different domain. We chose the example of a computer game since it clearly demonstrates the attributes of our architecture: real-time action selection and handling uncertainty. Experimental results are presented for both implementations which show how the fuzzy method is applied, its generality and that it is robust enough to handle different uncertainties in different environments. In summary, the proposed reactive architecture is shown to solve aspects of behaviour control for autonomous virtual agents in virtual environments and can be applied to various application domains

Autonomous virtual agent navigation in virtual environments using dempster shafer approach and fuzzy logic

This paper presents a solution for behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer’s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or to identify which part of an obstacle can be seen from the position of the virtual agent. This information is required for virtual agent to coordinate navigation in virtual environment. The virtual agent uses fuzzy controller as a navigation system and fuzzy alpha-level for the action selection method. The testing was divided into two parts namely navigating in complex environment using different degrees of uncertainty and measuring the effectiveness of proposed action selection method to coordinate the behaviours by comparing with Fuzzy Behaviour Fusion (FBF) method. The aim of the testing was to evaluate the performance in terms of robustness and quality of path generated by the virtual agent. The result clearly demonstrates that the path produced is reasonably smooth even though there is some sharp turn and not diverted too far from the potential shortest path. This indicates the strength of our method, where more reliable and accurate paths produced during navigation task

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN\u27s learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models

Variance-covariance based weighing for neural network ensembles

Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter\u27s initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy