Running Head: MAS. The multi-agent system for Airline reservation using voice commands

Transcription

1 Running Head: MAS i The multi-agent system for Airline reservation using voice commands by [Author s Name] [Faculty Name] [Department or School Name] [Month Year]

2 MAS ii ABSTRACT The paper highlights the benefits of Multi Agent System for maintaining for airline reservation using voice recognition. It establishes the effect of degree of distribution of agents on system performance. It also establishes the relationship between the social attitude of an agent towards the other agents and fairness of resource distribution in distributed architecture.

3 MAS iii TABLE OF CONTENTS ABSTRACT... ii Introduction... 1 Related work... 1 Aim and Objectives... 2 Agent s goals and sub-goals... 3 Research Questions... 5 Methodology... 5 Timeline (Gantt chart)... 5 REFERENCES... 7

4 MAS 1 The multi-agent system for Airline reservation using voice commands Introduction A Multi Agent System (MAS) comprises of a set of agents, which interact, collaborate, cooperate, or even negotiate with each other and with the environment to solve a particular problem in a coordinated manner (Bukhari & Kim, 2012). The work presented in the paper contributes by demonstrating the ability of MAS in improving or maintaining the quality of service as required by cellular network provider. This is achieved through agent interaction, for various call admission control strategies for different traffic classes. Related work A multi-agent system is a system that is structured as a set of autonomous agents that are able to flexibly adapt their behavior to changing operating conditions. Individual agents have only limited knowledge and control over the system as a whole. To achieve the overall system functionalities and qualities, agents interact and coordinate their behavior. Architectural design of a multi-agent system concerns the concrete specification of the top-level structures of the system in order to achieve the stakeholders requirements. Structures include the primary structures of individual agents and the structures of organizations of agents. When introducing a multi-agent system, it must be integrated with its environment (common frameworks, legacy systems, etc.). In Egemin,.NET is the standard environment and the company uses an in-house-developed component framework that provides common middleware services. Examples of legacy systems with which the multi-agent system needed to be integrated are the ERP system that generates the transport tasks and the steering system that provides the low-level control software of the AGVs. Dealing with these constraints raised severe challenges during the design and implementation of the multi-agent system.

5 MAS 2 Fig.Architecture design and documentation in the software development life cycle. As a result of airline deregulation in 1978, airlines have been forced to become more competitive as an industry. From a strategic management standpoint, prior to deregulation, the airline industry was at a less turbulent level. Environmental turbulence is a combined measure of the discontinuity and predictability of the firm's environment. From a broad perspective, the threats and opportunities in the environment force the company to analyze its strengths and weaknesses in order to respond to that particular environment. Management must scan the environment and be able to make decisions based upon the amount of uncertainty present. Utilizing Ansoff s model of Environmental Turbulence, it appears that the airlines were at a level two, prior to deregulation, described as expanding and slow incremental change. The responsiveness of general management capability would be efficiency driven and reactive to the degree of change. Management would adapt to change at this level. After deregulation, however, the environment shifted to a more competitive nature, driven by competition and an open market. Changes to accommodate customers were also issues that were important. Thus, the airline industry shifted to Ansoff s level-three environmental turbulence. At this level, environmental turbulence is classified as changing and fast incremental change. The responsiveness of general management capability would be market driven and management would anticipate and seek familiar change, and would be more proactive. All of a sudden, a customer focus would be required (Freeman & Kennewick, 2013). This shift in environmental turbulence from level 2 to level 3 would cause the airlines to evaluate the firm s present behavior and forecast the future potential success behavior. This is called a strategic diagnosis. The strategic diagnosis is based upon three variables: the environment, the internal capability of the firm and its ability to react to the external strategic response that may be required. The diagnosis is based on an empirically proven strategic success formula which states that a firm will be successful if its internal capability matches its strategic response to the environment, and capability and response match the level of turbulence in the environment. The larger the gap, the more intense the firm must transform the business to improve its position. Some key areas that are important in this analysis would include market structure, share and development, responsiveness to competitors and customers, product image differentiation, investment in marketing and research, the firm s management leadership style, risk propensity, knowledge and problem solving capabilities, the culture of the firm, its values and reward systems, the structure and flexibility, and information and decision systems. Thus, management success profiles must change as well (Gold, Morgan & Ellis, 2011). The level two environmental turbulence would require a controller type of manager whose goal would be to minimize costs, have a present time perspective and attempt to control risk. At a level three, this manage must change to a planner type whose goal would be to optimize profit, respond to customers, have a future time perspective, and be open to familiar risk. Aim and Objectives This research study intended to examine a multi-agent system for Airline reservation using voice commands. Moreover, this research study examine the design, and architecture of system using multi-agents to achieve that i.e. an agent for customer reservation, one for managing reservations, also an administrator for managing flights, also every agent linked with the database for reservation data has goals and sub-goals to achieve it.

6 MAS 3 Agent s goals and sub-goals MAS based Service Architectures depending on the degree of distribution of agents and the type of interaction, in Connection Plane:

7 MAS 4 The current state of speech recognition is such that it performs very well when it has few options in the grammar file, but more poorly when it has many. Thus, a speech recognition engine instructed to recognize either yes or no will succeed virtually every time. When the recognition engine must choose between 25 template questions, it chooses incorrectly occasionally, even when the spoken input is identical to a template question. When the recognition engine is allowed to create phrases out of component words, it is likely to make considerably more recognition errors (Zirngibl et al., 2011). A system with full question recognition should have reasonably good recognition performance. If a question is phrased properly, the recognition engine will often choose the correct template, resulting in a meaningful answer. However, if the question is worded differently from a template wording, the full question recognition is more likely to fail. A system with component recognition is likely to have poorer speech recognition performance. Since each word of the spoken input may be matched to any word on the list, the recognition engine makes errors much more frequently. However, if a question is worded in an unexpected manner, but contains many of the same words as a template, the component recognition may succeed in selecting a logically similar although structurally different question, where full question recognition fails. By the turn of the twentieth century, question answering had become recognized as a field of its own. Researchers differentiate between question answering systems on a number of levels. A closed-domain question answering system is designed to answer questions about a particular topic or area. Open-domain systems attempt to answer questions about any topic. The data containing the answers to the questions may be a large or small collection. Most current research has focused on large collection systems, particularly where the collection is the Web. Most Web question answering systems return a document or list of documents. Some return a portion of a document, commonly referred to as a snippet, which contains the answer. A small amount of research has been done on systems that construct answers. Question answering systems can use typed or spoken input (Gold, Morgan & Ellis, 2011).

8 MAS 5 In 1999, the Text Retrieval Conference (TREC, co-sponsored by the NIST and the US DOD), began its question answering track, allowing developers to compete and compare methodologies. Each year, the conference offers a large collection of text data from newspapers and various agencies, and a list of questions. The set is used to evaluate open-domain, large collection, typed-input question answering systems. Participants test systems that return snippets. The TREC QA track questions and data sets are also used by many developers and researchers who are not participants for system evaluation (Bukhari & Kim, 2012). Research Questions What is the multi-agent system? How it is used for Airline reservation using voice commands? Methodology Secondary research method is adopted for this study. The adoption of this method ensures a key objective. The secondary research highlight what is already known and what are the gaps in the literature. When the finding of the current study is presented, the researcher triangulated them with the literature review to identify similarities and differences. The most influential theoretical perspectives for a research study have been positivism and interpretivism. Intepretivism looks for interpretations of the world that are historically situated and culturally derived. A research design is the strategy for a study as well as the plan stipulating a guideline, a strategy to carry out study. It specifies the methods and procedures for the collection, measurement, and analysis of data. It is one thing to make a decision beforehand and another to evaluate the basis for making decision. Better the method used result in better decisions. A researcher should design his research if he wants to obtain useful results. Today a good research cannot be undertaken without developing a sound research methodology, which must match the problems and hypotheses of the study. The methodology includes not only the various steps for conducting research but also states clearly the purpose of research and reviews the available literature on the topic. In addition, a good research methodology provides good arguments for selecting any research techniques accessible to the researcher. This is why research methodology provides a good rationale for conducting research and all the procedures to be followed in conducting the research including the selection of a sampling design, if required. Research design today is an essential part of how the data should be collected. In other words, research methodology includes the purpose of the research with full justifications about the objectives of the research and what is achieved by conducting the research. The researcher must be clear in his mind, before conducting the research, about the purpose and value of the research. From the point of view of the researcher this is one of the steps before conducting business or social research to have some understanding of the possible outcomes of the research. The second part of the methodology, research design, is the crux of the research activities. The third part, which is most crucial as well as critical, is the use of statistical or other analytical techniques to draw conclusions from the research studies. Timeline (Gantt chart) TASKS Months

9 MAS 6 Construct research proposal Draw up questionnaires Submit proposal Timetable interviews Begin interviews, observations, focus groups and hand out questionnaires Analysis and redefine problem(s) Implement findings Prepare draft report Begin full data analysis Write 12,000 word dissertation

10 MAS 7 REFERENCES Bukhari, A. C., & Kim, Y. G. (2012). Integration of a secure type-2 fuzzy ontology with a multiagent platform: a proposal to automate the personalized flight ticket booking domain. Information Sciences, 198, Freeman, T., & Kennewick, M. (2010). U.S. Patent No. 7,818,176. Washington, DC: U.S. Patent and Trademark Office. Freeman, T., & Kennewick, M. (2012). U.S. Patent No. 8,145,489. Washington, DC: U.S. Patent and Trademark Office. Freeman, T., & Kennewick, M. (2013). U.S. Patent Application 14/016,757. Freeman, T., & Kennewick, M. (2013). U.S. Patent No. 8,527,274. Washington, DC: U.S. Patent and Trademark Office. Gold, B., Morgan, N., & Ellis, D. (2011). Speech and audio signal processing: processing and perception of speech and music. John Wiley & Sons. Kennewick, R. A., Locke, D., Kennewick, M. R., Kennewick, R., & Freeman, T. (2012). U.S. Patent No. 8,112,275. Washington, DC: U.S. Patent and Trademark Office. Kennewick, R. A., Locke, D., Kennewick, M. R., Kennewick, R., & Freeman, T. (2012). U.S. Patent No. 8,140,327. Washington, DC: U.S. Patent and Trademark Office. Langseth, J., Orolin, N. J., Richards III, F., Patnaik, A., Saylor, M. J., & Zirngibl, M. (2011). U.S. Patent No. 7,881,443. Washington, DC: U.S. Patent and Trademark Office. Silva, D. F., de Souza, V. M., Batista, G. E., & Giusti, R. (2012). Spoken digit recognition in portuguese using line spectral frequencies. In Advances in Artificial Intelligence IBERAMIA 2012 (pp ). Springer Berlin Heidelberg. Yeracaris, Y., Gray, P. M., & Dreher, J. P. (2013). U.S. Patent No. 8,484,031. Washington, DC: U.S. Patent and Trademark Office. Zirngibl, M., Patnaik, A., Maass, B., Eberle, H., & Langseth, J. (2011). U.S. Patent No. 8,051,369. Washington, DC: U.S. Patent and Trademark Office.