A dissertation Report on
AVATAR SIMULATED BOT: WITH SEMANTIC MEMORY AND CONGNITIVE APPROACH
SAI-SIMULATED ARTIFICIAL INTELLIGENCE BOT
A dissertation Report on
AVATAR SIMULATED BOT: WITH SEMANTIC MEMORY AND COGNITIVE APPROACH
PARUL INSTITUTE OF ENGINEERING AND TECHNOLOGY (MCA)
VADODARA
(2011–2012)
by:-
095250693053 095250693058
Anitha .K. Swamy Patel Nikisha A
ACKNOWLEDGEMENT
The 5th Semester Dissertation is a golden opportunity for learning, doing research work, understanding & implementation of new technologies and self development. We consider ourselves very lucky and honored to have so many wonderful people lead us through in completion of this dissertation.
Mr. V.N.Acharya, HOD, MCA Dept. and our Dissertation Guide Falguni Ranadive monitored our progress and arranged all facilities to make dissertation easier. She was always so involved in the entire process, shared his knowledge, and encouraged us to think. Thank You, Dear Madam. We choose this moment to acknowledge her contribution gratefully.
Last but not the least, there were so many who shared valuable information that helped in successful completion of this dissertation. I would like to thank all of them on behalf of us both team members.
TABLE OF CONTENTS
GLOSSARY OF IMPORTANT TERMS AND ABBREVIATIONS
AVATAR
SAI
ARTIFICIAL INTELLIGENCE
EMBODIED AGENTS
AIML: ARTIFICIAL MARKUP LANGUAGE
AIML, orArtificial IntelligenceMarkup Language, is anXMLdialect for creatingnatural languagesoftware agents.
1.)LIST OF FIGURES AND CHART
Then following figures shows the concept of backpropogation.
A simple agent program which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions:
Agent function is an abstract concept as it could incorporate various principles of decision making like calculation ofutilityof individual options, deduction over logic rules,fuzzy logic, etc.
Theprogram agent, instead, maps every possible percept to an action.
Artificial embodied agents are often described schematically as above
Simple embodied agents
Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on thecondition-action rule: if condition then action. This agent function only succeeds when the environment is fully observable
Self-Learning Embodied agents
Learning has an advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow. The most important distinction is between the "learning element", which is responsible for making improvements, and the "performance element", which is responsible for selecting external actions.
The learning element uses feedback from the "critic" on how the agent is doing and determines how the performance element should be modified to do better in the future. The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions.
The last component of the learning agent is the "problem generator". It is responsible for suggesting actions that will lead to new and informative experiences.
2.) MAIN TEXT
2.1ABSTRACT:
This dissertation introduces a unified data acquisition, processing and synthesisframework for the creation of biomechanically correct human avatars
2.2 KEY TERMS :
Avatar, SimulatedArtificialIntelligence bot,artificial intelligence,bot,chatbot assistant, virtualassistant ,semantic memory, embodiedconversational agent.
2.3 INTRODUCTION:
One of the most significant roles played by technology is connectingpeople and mediating their communication with one another. Remoteconversations were unthinkable beforebut are now routinely conducted with devices ranging from two-waypersonal computers to videoconference systems.
Building technology that mediates conversation presents a number ofchallenging research and design questions. Apart from the fundamentalissue of what exactly gets mediated, two of the more crucial questions arehow the person being mediated interacts with the mediating layer and howthe receiving person experiences the mediation (see Figure 1). This literary introduction provides the framework of mediated conversation by means of automated avatars.
A lot of efforts in constructing interfaces based on natural language have been devoted to creating a simulated pseudo bot that the user seems to feel that the bot understands the meaning of thewords. Since the famous “Eliza” program of Weizenbaum chatterbots attempt to discover keywords and sustain dialog by asking pre-prepared questionswithout understanding the subject of conversationor the meaning of individual words. This is quite evidentfrom the Loebner prize chatterbot competition, popularity of bots based on AIML language, andthe general lack of progress in text understanding andnatural language dialogue systems. Cheating has obviouslyits limitations and it is doubtful that good naturallanguage interfaces may be built this way. An alternativeapproach used by humans requires various types ofmemory systems to facilitate concept recognition,building episodic relations among concepts, and storingthe basic information about the world, descriptions ofobjects, concepts, relations and possible actions in theassociative semantic memory. Although the propertiesof semantic memory may be partially captured by semanticnetworks so far this has been demonstrated only in narrow domains , and it is not easy to see how tocreate a large-scale semantic network that could beused in an unrestricted dialog with a chatterbot.
In this paper cognitive inspirations are drawn upon
to make a first step towards creation of avatars
equipped with semantic memory that will be able to use
language in an intelligent way. This requires ability to
ask questions relevant to the subject of discourse, questionsthat constrain and narrow down possible ambiguities.Very ambitious projects that usea sophisticated frame-based knowledge representation,
have been pursued for decades and can potentially becomeuseful in natural language processing, although
this has yet to be demonstrated. However, the complexityof the knowledge-based reasoning in large systemsmake them unsuitable for real-time tasks, such as quickanalysis of large amounts of text found on the webpages, or simultaneous interactions with many users.
An alternative strategy followed here is to start from
the simplest knowledge representation for semanticmemory and to find applications where such representationis sufficient. Drawing on its semantic memory anavatar may formulate and may answer many questionsthat would require exponentially large number of templatesin AIML or other such languages.
Endowing avatars with linguistic abilities involvestwo major tasks: building semantic memory model, andproviding all necessary means for natural communication.
This paper describes our attempts to create HumanizedInterface based on a 3D human facemodel, with speech synthesis and recognition, which isused to interact with Web pages and local programs,making the interaction much more natural than typing these actions are based primarily on the information inits semantic memory. Building such memory is not asimple task and requires development of automatic andmanual data collection and retrieval algorithms, usingvarious tools for analysis of natural language sources.
Avatar :SAI-SIMUALTED ARTIFICIAL INTELLIGENCE BOT
Incomputing,anavataris thegraphical representationof theuseror the user'salter egoorcharacter. It may take either adimensional form, as ingamesor virtual worlds, or a two-dimensional form as aniconinInternetforumsand other online communities.It can also refer to a text construct found on early systems such asMUDs.It is an object representing the user. The term "avatar" can also refer to the personality connected with thescreen name, or handle, of an Internet user.
Avatar chatbotis anyfictional characternot controlled by a person who created this usually means a character controlled by the computer throughartificial intelligence.Inartificial intelligence, anembodied agent, also sometimes referred to as an interface agent,is anintelligent agentthat interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, Embodied conversational agentsare embodied agents (usually with a graphical front-end as opposed to a robotic body) that are capable of engaging in conversation with one another and with humans employing the same verbal and nonverbal means that humans do (such as gesture, facial expression, and so forth).
One of the trends of recent years has been the humanizing of digital channels, giving a face to things which are not human. This has led to the creation ofavatars(also known asbotsorchatter-bots) artificial intelligences with which users can “converse”. The success of such bots varies greatly, there are few which respond in a convincingly human way, it is no great mystery why they are commonly referred to as “Bots” often resulting in a stilted, mechanical interaction where straying off a recognized path can lead to poor responses
However, this has not stopped their spread across the commercial world with several high profile companies adopting them as part of their customer services. An avatar such asSAI BOT render to the idea of an artificial intelligence able to respond in an intelligent manner to your questions is indeed an exciting one. However, do these bots really manage it? Or are they just human faced Avatars disguising a search engine beneath?Or such as a intelligent virtual assistant may basically consist of adialog system, anavatar, as well anexpert systemto provide specific expertise to the user. Other componentsAn automated online assistant also has anexpert systemthat provides specific service, whose scope depends on the purpose of it.Also,serversand other maintaining systems to keep the automated assistant online may also be regarded as components of it.
History
An avatar is a user’s visual embodiment in a virtual environment. Theterm, borrowed from Hindu mythology where it is the name for thetemporary body a god inhabits while visiting earth, was first used in itsmodern sense by Chip Morningstar who along with Randall Farmercreated the first multi-user graphical online world Habitat in 1985 (Damer 1998). Habitat was a recreational environment where people could gatherin a virtual town to chat, trade virtual props, play games and solve questsUsers could move their avatars around the graphical environment usingcursor keys and could communicate with other online users by typingshort messages that would appear above their avatar. Habitat borrowedmany ideas from the existing text-based MUD environments, but thevisual dimension added a new twist to the interactions and attracted a newaudience (Morningstar and Farmer 1990). Avatar-based systems sinceHabitat have been many and varied, the applications ranging from casualchat and games to military training simulations and online classrooms.
In electronic media such as chat bots, this usually means a character controlled by the computer throughartificial intelligence.
Web 2.0 Avatars, powered by Digital Conversations, provide a level of immersion not found in these bots. Why? Because Digital Conversations are scripted just like any good book. And like books they are designed to guide a user, through high quality dialogue and interactions, to an outcome. Along with this, the ability to understand user interactions through DecisionMetrics means that these Web 2.0 Avatars can be adapted to emergent demands as they appear. The dialogue can be improved and built up as and when needed.High quality dialogue, clear concise options for a user to choose and a humanized avatar all combine to create an immersive experience, with the psychological appeal of interacting with a character or object .
The key to immersion and believability is high quality dialogue, and it is high quality dialogue that Digital Conversations has been created for.
FUNAMENTAL CONCEPTS
ARTIFICIAL INTELLIGENCE:
Artificial intelligence(AI) is theintelligenceof machines and the branch ofcomputer sciencethat aims to create it. Traditionally it is defined as the field of "the study and design of intelligent agents"where an intelligent agentis a system that perceives its environment and takes actions that maximize its chances of success.John McCarthy, who coined the term in 1956,defines it as "the science and engineering of making intelligent machines."
Classified and statistical learning methods employed for functioning of the avatar
Neural networks
Main articles:Neural networkandConnectionism
A neural network is an interconnected group of nodes, akin to the vast network ofneuronsin thehuman brain.
The study ofartificial neural networksbegan in the decade before the field AI research was founded, in the work ofWalter PittsandWarren McCullough. Other important early researchers wereFrank Rosenblatt, who invented theperceptronandPaul Werboswho developed theback propagationalgorithm.
The main categories of networks are acyclic orfeedforward neural networks(where the signal passes in only one direction) andrecurrent neural networks(which allow feedback). Among the most popular feedforward networks areperceptrons,multi-layer perceptronsandradial basis networks.Among recurrent networks, the most famous is theHopfield net, a form of attractor network, which was first described byJohn Hopfieldin 1982.Neural networks can be applied to the problem ofintelligent control(for robotics) orlearning, using such techniques asHebbian learningandcompetitive learning.Hierarchical temporal memoryis an approach that models some of the structural and algorithmic properties of theneocortex.
Intelligent agent paradigm
Anintelligent agentis a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that respond to real life entity based questions such agents are symbolic and logical,neural networksand others may use new approaches. The paradigm also gives researchers or users ability to communicate with such embodied agents (use this for neural net descript )Artificial Neural Networks (ANNs) are a new approach that follow a different way from traditional computing methods to solve problems. Since conventional computers use algorithmic approach, if the specific steps that the computer needs to follow are not known, the computer cannot solve the problem. That means, traditional computing methods can only solve the problems that we have already understood and knew how to solve. However, ANNs are, in some way, much more powerful because they can solve problems that we do not exactly know how to solve. That's why, of late, their usage is spreading over a wide range of area including, virus detection, robot control, intrusion detection systems, pattern (image, fingerprint, noise..) recognition and so on.
ANNs have the ability to adapt, learn, generalize, cluster or organize data. There are many structures of ANNs including, Percepton, Adaline, Madaline, Kohonen, BackPropagation and many others. Probably, BackPropagation ANN is the most commonly used, as it is very simple to implement and effective. In this work, we will deal with BackPropagation ANNs.
Backpropogation is used here as we are using aiml to create a pattern recognizing chatbot which will later on after long duration and consistent input of data by users will then result to unsupervised learning via output weights of backpropgation network being inputted as input of the neural net’s layersWe also make use of genetic algorithm tech.
Digital Conversation
ADigital Conversationis a scripted dialogue (in other words it is dialogue written by a human, just like the script of a movie) which takes place between a person and a computer via any digital medium fromweb browsersandPDAstomobile phonesandInteractive television.
2.4 LITERATURE SURVEY
2.5 MAJOR THESES AND HYPOTHESES PRESENTED
Reference from :Towards Avatars with Artificial Minds: Role of Semantic Memory
W The first step towards creating avatars with human-like artificial minds is to give them humanlike
memory structures with an access to general knowledge about the world. This type of
knowledge is stored in semantic memory. Although many approaches to modeling of semantic
memories have been proposed they are not very useful in real life applications because they lack
knowledge comparable to the common sense that humans have, and they cannot be implemented
in a computationally efficient way. The most drastic simplification of semantic memory leading
to the simplest knowledge representation that is sufficient for many applications is based on the
Concept Description Vectors (CDVs) that store, for each concept, an information whether a
given property is applicable to this concept or not. Unfortunately even such simple information
about real objects or concepts is not available. Experiments with automatic creation of concept
description vectors from various sources, including ontologies, dictionaries, encyclopedias and
unstructured text sources are described. Haptek-based talking head that has an access to this
memory has been created as an example of a humanized interface (HIT) that can interact with
web pages and exchange information in a natural way. A few examples of applications of an
avatar with semantic memory are given, including the twenty questions game and automatic
creation of word puzzles.
Reference : Avatar online augmented conversation (map chat alppimcation) for thesis and hypothesis
Major thesis (ABSTRACT)
This thesis is concerned with both of thesequestions and proposes a theoretical framework of mediated conversation by means of automated avatars.This new approach relies on a model of face-to-face conversation, and derives an architecture forimplementing these features through automation. First the thesis describes the process of face-to-faceconversation and what nonverbal behaviors contribute to its success. It then presents a theoreticalframework that explains how a text message can be automatically analyzed in terms of its communicativefunction based on discourse context, and how behaviors, shown to support those same functions in face-tofaceconversation, can then be automatically performed by a graphical avatar in synchrony with themessage delivery. An architecture, Spark, built on this framework demonstrates the approach in an actualsystem design that introduces the concept of a message transformation pipeline, abstracting function frombehavior, and the concept of an avatar agent, responsible for coordinated delivery and continuousmaintenance of the communication channel. A derived application, MapChat, is an online collaborationsystem where users represented by avatars in a shared virtual environment can chat and manipulate aninteractive map while their avatars generate face-to-face behaviors. A study evaluating the strength of theapproach compares groups collaborating on a route-planning task using MapChat with and without theanimated avatars. The results show that while task outcome was equally good for both groups, the groupusing these avatars felt that the task was significantly less difficult, and the feeling of efficiency andconsensus were significantly stronger. An analysis of the conversation transcripts shows a significantimprovement of the overall conversational process and significantly fewer messages spent on channelmaintenance in the avatar groups. The avatars also significantly improved the users’ perception of eachothers’ effort. Finally, MapChat with avatars was found to be significantly more personal, enjoyable, andeasier to use. The ramifications of these findings with respect to mediating conversation are discussed.