Intelligent traveling agent system for air lines reservation.
IntroductionArtificial Neural Networks (ANN) [1-4] have been applied for large number of applications including engineering, science & technology, biomedical, financial forecasting etc. [5-7] Due to their properties of inherent massive parallelism capability to solve non linear problems, classifications, regressions and fault tolerance. ANN is being applied to various areas. This paper applied it to design a software agent in terms of an intelligent Travel Agent [8-10].
Artificial Intelligence tries to overcome the criticisms that computers are not intelligent and they can do exactly what they are programmed for. Expert Systems have emerged as a result and have been applied successfully in various areas.
ANN works very similar to the biological neurons of human brain and its charters tics is also similar to biological neurons. In most of the application of ANN have been applied to predict the events based on their past history. ANN based systems are first trained for a given set of data training set and then tested for unknown pattern. In a manual reservation the operator (Clerk) receives information from all passengers and clients then it forwards to the concerning section for appropriate action. There are always chances that the operator may ignore, forgot, or misunderstand a certain email. The proposed work in the paper develops an intelligent agent based on the principles of ANN. The paper is organized as follows. Section 2 outlines software agents. Section 3 gives a basic and brief understanding of ANN. Section 4 defines the problem. Proposed scheme has been explained in Section 5. Computer simulation of the proposed scheme has been carried out in Section 6 and testing with results has been shown in Section 7. Some conclusions, limitations are mentioned in Section 8.
Software Agent
Software agents are the agents, which are programmed to react judiciously with preprogrammed intelligence. Software Agents, which learns users' interests and can, act autonomously on their behalf to contribute in solving the problems.
Definitions
"The term agent is used to represent two orthogonal concepts. The first is the agent's ability for autonomous execution. The second is the agent's ability to perform domain oriented reasoning." [11]
"An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors." [12]
"Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." [13]
"Let us define an agent as a persistent software entity dedicated to a specific purpose. 'Persistent' distinguishes agents from subroutines; agents have their own ideas about how to accomplish tasks, their own agendas. 'Special purpose' distinguishes them from entire multifunction applications; agents are typically much smaller. II [14]
"Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires". [15]
"Hardware or (more usually) software-based computer system that carries the following properties:
* Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state;
* Social ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language;
* Reactivity: agents perceive their environment, (which may be the physical world, a user via a graphical user interface, a collection of other agents, the INTERNET, or perhaps all of these combined), and respond in a timely fashion to changes that occur in it;
* Pro-activeness: agents do not simply act in response to their environment; they are able to exhibit goal-directed behavior by taking the initiative. (16)
"Autonomous agents are systems capable of autonomous, purposeful action in the real world. II (17)
Agent Classifications
The various definitions discussed above involve a host of properties of an agent. Having settled on a much less restrictive definition of an autonomous agent, these properties may help us further classifies agents in useful ways. The table that follows lists several of the properties mentioned above.
Property Other Names Meaning
Reactive (Sensing and Responds a timely fashion to changes
acting) in the Environment
Autonomous Pro-active Exercise control its own actions
Goal-oriented purposeful Does not act in response to the
environment
Temporally Is a continously running process
continuous
Communicative Socially able Communicates with other agents, perhaps
including people
Learning Adaptive Changes behavior based on its
previouse experience
Mobile Able to itself from one machine
to another
Flexible Actions not scripted
Character Believable "personality" and emotional
state.
Brustoloni's taxonomy of software agents [1991] begins with a three-way classification into regulation agents, planning agents, or adaptive agents. A regulation agent, probably named with regulation of temperature by a thermostat or similar regulation of bodily homeostasis, reacts to each sensory input as it comes in, and always knows what to do. It neither plans nor learns. Planning agents plan, either in the usual AI sense (problem solving agent), or using the case-based paradigm (case-based agents), or using operations research based methods (OR agents), or using various randomizing algorithms (randomizing agent). Brustoloni's adaptive agents not only plan, but also learn. Thus there are adaptive problem solving agents, and so on, yielding a two-layer taxonomy.
Agents may be usefully classified according to the subset of these properties that they enjoy. Every agent, by our definition, satisfies the first four properties. Adding other properties produces potentially useful classes of agents, for example, mobile, learning agents. Thus a hierarchical classification based on set inclusion occurs naturally. Mobile, learning agents are then a subclass of mobile agents.
There are, of course, other possible classifying schemes. For example, we might classify software agents according to the tasks they perform, for example, information gathering agents or email filtering agents or, we might classify them according to their control architecture. Sumpy, then, would be a fuzzy sub sumption agent, while Etzioni and Weld's Softbot would be a planning agent [1994]. Agents may also be classified by the range and sensitivity of their senses, or by the range and effectiveness of their actions, or by how much internal state they possess.
Yet another possible classification scheme might involve the environment in which the agent finds itself, for example software agents as opposed to artificial life agents. And, there must be many, many more such possibilities. Which one, or ones, shall we choose?
[FIGURE 1 OMITTED]
Artificial Neural Network ANN
ANN [1..4] are interconnected collections of simple, independent processors. While loosely modeled after the brain, the details of neural network design are not guided by biology. Instead, for over 20 years researchers have been experimenting with different types of nodes, different patterns of interconnection and different algorithms for adjusting connections. Neural networks are called Machine-Learning algorithms because changing these connections (training) causes the network to learn the solution to a problem. This differs from other artificial intelligence technologies, such as expert systems, fuzzy logic or constraint-based reasoning which must be programmed to solve a problem. Many different neural network models have been explored. These models are described as either unsupervised or supervised. Unsupervised neural networks, such as self-organizing feature maps, find relationships between input examples by examining the similarities and differences between the examples. Supervised neural networks, such as back propagation [1-4], are used for pattern recognition or prediction. For supervised neural networks, the input examples must be accompanied by the desired output. Typically a network consist of a Input layer and Output layer then it is called Single layer perceptron (SLP) (Fig 2) .If a network consist of a set of sensory units (Source nodes) that constitutes the 'Input Layer', one or more 'hidden layers' in computation nodes and an 'Output Layer' of computation nodes the input signal propagates through in a forward direction, on a layer - by - layer basis, These neural networks commonly referred as multilayer preceptron (MLP)(Fig 3).
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
Problem Definition
The main objective of this paper is to design an agent which can replace a manual travel computer operator. The problem can be divided into following steps
(1) Collection of emails from web server.
(2) Reading the contents of each individual.
(3) Trained a Neural Network to understand the contents read in order to classify into one of the three categories.
(4) Forward the email to the concern section dealing with one of the three categories.
Due to varieties in style and language to be used in various countries, it is fare to admit the success cannot be guaranteed 100% .
Proposed Scheme
Figure 4 shows the adopted to design the agent. Below mentioned algorithm is used to design the agent.
(a) Collect all the emails from mailbox. It is assumed that one mail one purpose.
(b) Check each email one by one.
(c) If agent found a new word then it will direct to user to specify the category.
(d) Agent is trained by pre available patterns. if it get any email then it will categorize it according to its training.
[FIGURE 4 OMITTED]
Simulation of proposed scheme
The proposed scheme is stimulated through computer. Emails were received and read using Visual Basic. Every email is composed of sentences separated by period and words are separated by space. Here normal convention has been used. There are three categories of active words. These are Reservation. Confirmation and Cancellation. Thus there will be four databases in all. Three for active category and the last for 'Neutral category'. Each word of the sentence is marked as a active or inactive by their occurrence in database .If any word is active then the whole sentence will be treated as active sentence. One active sentence in email will make it active email. An email with no active sentences is treated as neutral and consequently any email having no active sentence will be categorized as a neutral category. Every word of a database has given a numeric constant value for a clear distinction among the weight values of the words in database. Words in one database are given values with a good margin with those in the other databases. This will have two advantage:; first there will be space for marking new word
in the same database, second training become easier due to discrete wide range of numbers. The databases with their weights and words are shown.
The input to a neural network is a pattern consisting of the weight values of the words in an active sentence. The size of input layers has been fixed to 10 because a normal sentence does not have more than ten words. The output of the neural network is the category, which this active sentence refers. There are three nodes in output layer. Each representing one of the active categories. For the active input the corresponding output node will be set to 1 and remaining with 0.In case of Multi Layer Perceptron (MLP) there are three neurons in hidden layer. If an active sentence has less then ten words then extra nodes of neural network are given value of the inactive words that is -1 .
(i) 100 Reservation
(ii) 010 Confirmation
(iii) 001 Cancellation
If a new word is encountered and this word is not found in any of the four databases then the Agent will direct this word to the user for classification, which will be added to one of the four databases.
It is the front face of Agent. This interface is divided into four parts. These are (i) This part will take the email address and password of the travel agent including the mail server name (which supports the POP3 protocol).
(ii) It is a grid, which will show the incoming emails from clients. This grid has four columns. They are "FROM","SUBJECT","DA TE","SIZE and "ID".
(iii) This part will show the current email contents, which is on the processing. It is just like a monitoring tool.
(iv) The fourth and final part are consist of four boxes. These are the result boxes. After processing Agent will get the category of email than it will put the email address on the corresponding box.
Three command buttons are also part of the interface. These are Check Email -It will check the mailbox and fill the grid with appropriate details. Categorize - It will direct the active sentences to ANN (Artificial Neural Networks)
Exit - It is used to close the Agent system.
[FIGURE 6 OMITTED]
Curve shown in fig 6 is produced by MATLAB. This graphs show that the performance goal is achieved.
New York RESERVATION CONFIRMATION CANCELLATION GARBAGE
It is the third interface, which will appear when a new word will found. As it, shows four options for user to categorize the world.
Testing and Results
The proposed agent program used 500 emails for training and 50 emails for used for testing. Some Tasting results shown below. From this table it is clear that the agent design using proposed scheme is capable to recognize 94 % of the emails correctly with an error rate of 6 %
Conclusions
ANN which has been extensively used in different areas have been applied to design an intelligent agent. This paper proposed application of neural network to design an intelligent travel agent. This agent is supposed to receive emails from clients spread out over the country and direct to concerning section of travel centre. The sentences of email are categorized as an active or inactive on the basis of words in it. The active sentence is used as an input to neural network where as the inactive sentences are stored in neutral category .The ANN is trained to predict the category of active sentence. In this scheme there are also provision to include a new unknown word into of the database by the user directive. For training of ANN SLP and MLP have been used and the performance of the each is satisfactory. The training has been carried out on a large number of emails to include as many sample patterns as possible. Tasting has also been done with fair results. It is to admit here that the variation in style and language may affect the results minutely or majority. This limitation opens the scope for researchers.
References
[1] B.Kosko Neural Networks and Fuzzy systems, Englewood Cliffs,NJ:Prentice Hall 1991
[2] Wasserman,P (1989) "Neural Computing Theory and practice",Van no strand Remhold ,New York
[3] L.A.Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing ", Communications of the ACM vol. 37,pp 77-84,1994.
[4] S.K.Pal ,Neuro- Fuzzy Pattern Recognition: Methods in soft computing, JohnWiley@sons 1999.
[5] Maes, Pattie (1995), "Intelligent-Agents: programa that ca act independent will ease the burdens that computers put on people," Scientific American vol 273,no 3,pp 66-68
[6] Etzioni, Gren, and Daniel Weld (1994), A Softbot-Based Interface to the Internet.Communications of the ACM, 37, 7, 72p; 79.
[7] www.NeuroXL.com [NeuroSolutions Excel]
[8] www.NeuroXL.com [NeuroXL Predictor to Predict Stock Prices]
[9] Stefan Wermter, Garen Arevian and Christo Panchev
[10] "Recurrent Neural Network Learning for Text Routing"
[11] The MuBot Agent (http://www.crystaliz.com/logicware/mubot.html]
[12] The AlMA Agent [Russell and Norvig 1995, page 33]
[13] The Maes Agent [Maes 1995, page 108]
[14] The KidSim Agent [Smith, Cypher and Spohrer 1994]
[15] The IBM Agent fhtto://activist.gpl.ibm.com:81/WhitePaper/ptc2.htm]
[16] The Wooldridgep;Jennings Agent [Wooldridge and Jennings 1995, page2]
[17] The Brustoloni Agent [Brustoloni 1991, Franklin 1995, p. 265]
(1) Arvind Dewangan, (2) Rajiv Kumar, (3) Vinod Kumar and (4) Maninder Singh
(1) Lecturer-Civil Engineering, Haryana College of Technology & Mgt., Kaithal Email-a.arvinddewangan@rediffmail.com
(2)Lecturer-Electrical & Electronics Engineering, Haryana College of Technology & Mgt., Kaithal. Email-rajivkumar333@gmail.com
(3) M.Tech.in Electronics & Communication Engineering,Student Of MMU,mullana Email-vinod_dhiman21@yahoo.com
(4) M.Tech.in Electronics & Communication Engineering,Student Of MMU,mullana Email-monti7022@mail.com
Table 1: Different options for maintaining Dictionary.
REERVATION CONFIRMATION CANCELLATION NEUTRAL
1 RESERVE 30 CONFIRM 50 CANCEL I
2 BOOK 31 CHECK 51 UNABLE . AM
3 GET 32 AVAILABILITY 52 POSTPONE PLZ
4 TAKE 33 FINAL 53 DROP WANT
5 RESERVATION 34 STATUS 54 WITHDRAW A
6 ARRANGE 55 BACK SEAT
56 CANCELLATION NEED
57 RETURN THE ME
TRIP
TO
LONDON
Table 2: Sample data obtained after applying Back Propagation
Algorithm through
MATLAB.
S.N. Email (Active Word only) Desired Neural Proposed
Category
1 PLEASE RESERVE A Reservation 1.00 0.856 0.523
SEAT
2 PLEASE GET A SEAT Reservation 0.99 0.822 0.500
FOR ME.
3 PLEASE GET A TICKET Reservation 0.992 0.556 0.723
FOR ME.
4 PLEASE TAKE A TICKET. Reservation 0.975 0.856 0.443
5 PLEASE TAKE A SEAT Reservation 0.905 0.656 0.263
FOR MUMBAI.
6 I WANT TO RESERVE A Reservation 0.903 0.576 0.583
SEAT.
7 I WANT TO BOOK A Reservation 0.992 0.899 0.565
SEAT.
8 I NEED A Reservation 0.900 0.89 0.523
RESERVATION.
PLEASE CONFIRM MY Confirmation 0.75 0.99 0.23
RESERVATION.
10 PLEASE CONFIRM MY Confirmation 0.57 0.992 0.35
B OOKING.
11 PLEASE CHECK THE Confirmation 0.55 0.963 0.60
AVAILABILTY OF
SEAT.
12 PLEASE CONFIRM THE Confirmation 0.78 0.912 0.35
TICKET.
13 PLEASE CONFIRM A Confirmation 0.75 0.915 0.23
SEAT.
WHAT IS THE
14 AVAILABILITY Confirmation 0.66 0.81 0.44
AGAINST
THE RESERVATION?
15 WHAT IS THE STATUS Confirmation 0.36 0.906 0.56
OF MY TICKET?
16 PLEASE CANCEL THE Cancellation 0.45 0.56 0.77
RESERVE SEAT.
17 PLEASE CANCEL THE Cancellation 0.40 0.33 0.89
BOOKING.
18 PLEASE POSTPONE THE Cancellation 0.65 0.20 0.89
Reservation.
19 PLEASE CANCEL THE Cancellation 0.66 0.52 0.929
TICKET.
20 PLEASE BACK THE Cancellation 0.78 0.565 0.95
TICKET.
21 I AM UNABLE TO GO. Cancellation 0.40 0.56 0.855
22 PLEASE DROP MY Cancellation 0.11 0.25 0.945
BOOKING.
23 I NEED TO CANCEL THE Cancellation 0.22 0.52 0.991
SEAT.
24 PLEASE RETURN MY Cancellation 0.20 0.63 0.902
TICKET.
25 PLEASE BOOK A TO Reservation 0.99 0.265 0.635
MUMBAI. .
26 PLEASE BOOK A Reservation -9.00 0.456 0.523
RETURN TICKET.
S.N. Email (Active Word only) Error
1 PLEASE RESERVE A No
SEAT
2 PLEASE GET A SEAT No
FOR ME.
3 PLEASE GET A TICKET No
FOR ME.
4 PLEASE TAKE A TICKET. No
5 PLEASE TAKE A SEAT No
FOR MUMBAI.
6 I WANT TO RESERVE A No
SEAT.
7 I WANT TO BOOK A No
SEAT.
8 I NEED A No
RESERVATION.
PLEASE CONFIRM MY No
RESERVATION.
10 PLEASE CONFIRM MY No
B OOKING.
11 PLEASE CHECK THE No
AVAILABILTY OF
SEAT.
12 PLEASE CONFIRM THE No
TICKET.
13 PLEASE CONFIRM A No
SEAT.
WHAT IS THE
14 AVAILABILITY No
AGAINST
THE RESERVATION?
15 WHAT IS THE STATUS No
OF MY TICKET?
16 PLEASE CANCEL THE No
RESERVE SEAT.
17 PLEASE CANCEL THE No
BOOKING.
18 PLEASE POSTPONE THE No
Reservation.
19 PLEASE CANCEL THE No
TICKET.
20 PLEASE BACK THE No
TICKET.
21 I AM UNABLE TO GO. No
22 PLEASE DROP MY No
BOOKING.
23 I NEED TO CANCEL THE No
SEAT.
24 PLEASE RETURN MY No
TICKET.
25 PLEASE BOOK A TO No
MUMBAI.
26 PLEASE BOOK A Yes
RETURN TICKET.
Figure 5: An Interface design by VB application For Reservation.
Simple Mail Reader
Remote Host User Namr Password Check
mailbox
Mail.softhome.net Travel Agent **********
Messages
FROM SUBJECT SENDDATE SIZE
CLIENT_1@Hotmail.com REGARDING 12 DEC 2005 1K
RESERVATION
CLIENT_2@Yahoo.com REGARDING 12 DEC 2005 1K
CONFIRMATION
CLIENT_3@MSN.com PLEASE CANCEL 12 DEC 2005 1K
Remote Host Category
Mail.softhome.net
Messages
FROM ID
CLIENT_1@Hotmail.com 125456
CLIENT_2@Yahoo.com 521452
CLIENT_3@MSN.com 256325
Please reserve me a ticket for GOA on Monday
RESERVATION CANCELLATION CONFIRMA GARBAGE
TION BOX
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| Author: | Dewangan, Arvind; Kumar, Rajiv; Kumar, Vinod; Singh, Maninder |
|---|---|
| Publication: | International Journal of Applied Engineering Research |
| Article Type: | Report |
| Date: | Nov 1, 2009 |
| Words: | 3355 |
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