1、1,Intelligent Agents,With Java,2,Focus of talk,A basic look at agent-based reasoning, modeling, and learning How agents can enhance the capability and productivity of commercial application software The effect of agents on the Web, with a Java twist,3,Artificial Intelligence: Introduction,The scienc
2、e of AI is approximately forty years dating back to a conference at Dartmouth in 1958 The public perception of AI has not always matched the reality The excitement of both scientists and the popular press tended to overstate the real-world progress of artificial intelligent systems Early success pro
3、mised rapid progress towards practical machines intelligence. Areas of early successes Game playing, mathematical theorem proving, common-sense reasoning, college mathematics,4,Introduction, contd.,AI research labs began specializing in narrow fields Speech recognition Natural language understanding
4、 Image optical character recognition The early successes were followed by a slow realization that things that humans do with very little effort was near impossible for the computer What was hard for people and easy for the computer was more than offset by the things that were easy for people to do b
5、ut almost impossible for computers to do,5,Introduction, contd.,The promise of the early years has never been fully realized The term artificial intelligence have become associated with failure and over-hyped technology Nevertheless, researchers in AI have made significant contributions to computer
6、science WIMP (Windows, icon, mouse, pointer) user interface Considered highly controversial and impractical when first introduce by the IA community Object-oriented programming techniques Refinement of the AI Frames concept,6,Basic Concepts,AI has always focused on problems which lie just beyond the
7、 reach of state-of-the-art computers Effectively pushing the current bleeding-edge technologies As computer science and computer systems evolved, the focus and areas which falls into AI research have also changed We can identify three major phases of development in AI research,7,First Phase,Much of
8、this work dealt with formal problems that were structured and had well-defined problem boundaries Math related skills: proving theorems, geometry, calculus, games (checkers, chess) Emphasis was on creating general “thinking machines” capable of solving broad classes of problems These systems tended
9、to include sophisticated capabilities relating to reasoning and search techniques,8,Second Phase,Marked by the recognition that the most successful AI projects were aimed at very narrow problem domains These systems usually encoded much specific knowledge about the problem to be solved This approach
10、 of adding specific domain knowledge to a more general reasoning system led to the commercial success in AI Expert Systems. Rule-based expert systems were developed to do many tasks Chemical analysis, configuring computer systems, diagnosing medical conditions in patients Suitable for repetitive and
11、 hazardous work Automated Process Control (Manufacturing Systems),9,Second Phase, contd.,Expert systems utilized research in a number of AI based discipline Knowledge representation, knowledge engineering, advanced reasoning techniques These systems proved that artificial intelligence could provide
12、real value in commercial applications Expert systems workstations with powerful integrated development environments were developed Lisp, Prolog, Smalltalk These were years ahead of commercial software development,10,Third Phase,Since the late 1980s much of the AI community has been working on solvin
13、g some difficult problems Machine vision and speech Natural language understanding and translation Commonsense reasoning and robot control Connectionism regained popularity and expanded the range of commercial applications through the use of neural networks for use in Data mining Modeling Adaptive c
14、ontrol,11,Third Phase, contd.,The AI playing field has been reenergized by biological methods such as genetic algorithms and alternative logic systems such as fuzzy logic Recent explosive growth in the Internet and distributed computing has led to the idea of Software Agents Software Agents are auto
15、nomous entities that move through the network, interacting with each other and performing tasks for their users,12,Intelligent Agents,Intelligent agents are software agents that use the latest AI techniques to provide autonomous, intelligent, and mobile software components, thereby extending the rea
16、ch of users across networks,13,Foot Note,Using commercial success as a measure of the value of technology is problematic to say the least I hypothesize that technology that is most beneficial to humanity on a whole will be the least commercially viable The rules of supply and demand will not apply t
17、o technologies that have the following characteristics Source is abundant (water for instance) The ability to transform and make readily available is attainable by every society Low technological barrier,14,What do we mean by intelligence?,Do we mean that our agents acts like a human? Think like a h
18、uman? That it acts or thinks rationally? There are as many answers as there are researchers involved in AI work From a software development perspective an intelligent agent is one that acts rationally primarily from a behavioral view point It does the things we do, but not necessarily the same way w
19、e would do them Our agent may not pass the Turing test as a yardstick for judging computer intelligence,15,Why AI Failed,This is only my opinion AI as we know it lacks a true model of cognition that can shed insights into events such as Correlation of facts, inference, and memory How the human brain
20、 work: higher level cognitive functions such as reasoning The Von Neumann model of a computer is a not a reasonable model of the brain and of human cognition,16,What do we mean by intelligence?,Our agents will perform useful tasks for us They will make us more productive They will allow us to do mor
21、e work in less time, and see more interesting information and less useless data Our programs will be qualitatively better using AI techniques than they would be otherwise The humble goal of intelligent agents is to develop better smatter applications,17,Areas to Explore,Symbol processing Neural netw
22、orks The Internet and the World Wide Web Events-Conditions-Actions,18,Intelligent Agents,Part-II,19,Intelligent Behavior,There are many behaviors to which we ascribe intelligence The ability to recognize situations or cases is a type of intelligence For example, a doctor who talks with a patient and
23、 collects information regarding the patients symptoms Then able to accurately diagnose an ailment and the proper course of treatment The ability to learn from a few examples and then generalize and apply that knowledge to new situations is another form of intelligence Intelligent behavior can be pro
24、duced by the manipulation of symbols,20,Symbol Processing,Symbol Processing is an AI technique Assertion: Intelligent behavior can be produced by the manipulation of symbols A primary tenets of AI techniques Symbols are tokens which represents real-world objects or ideas In this approach, a problem
25、must be represented by a collection of symbols An appropriate algorithm must then be developed to process these symbols,21,Symbol Processing, contd.,Physical symbol systems hypothesis Newell and Simon 1980 States that only a “physical symbol system has the necessary and sufficient means for general
26、intelligent action.” Basic thesis is that intelligence flows from the active manipulation of symbols This was the cornerstone on which much of the subsequent AI research was built Research built intelligent systems using symbols pattern recognition, reasoning, learning, planning History has shown th
27、at symbols may be appropriate for reasoning and planning Pattern recognition and learning are suited for other approaches,22,Manipulation of Symbols,Symbols in the formulations of If-Then rules Processed using forward and backward chaining reasoning techniques Forward chaining: system deduce new inf
28、ormation from a given set of input data Backward chaining: system reach conclusion based on a specific goal state Semantic Network Symbols and the concept they represent are connected by links into a network of knowledge that can then be used to determine new relationships Frames similar to Objects
29、in the OO paradigm Attributes of a concept are grouped together with related procedures for processing,23,Symbol Processing and Cognition,Symbol processing These techniques represent a relatively high level in the cognitive process Correspond to conscious thought, where knowledge is explicitly repre
30、sented, and the knowledge itself can be examined and manipulated Symbol-less approach An approach that is modeled after the brain,24,Neural Networks,This technique defines the connectionism camp of artificial intelligence More focus on how human or natural intelligence occurs Humans have neural netw
31、orks, consisting of hundreds of billions of brain cells called neurons Neurons are connected by adaptive synapses which act as switching systems between the neurons Artificial neural networks These are based on the massively parallel architecture found in the brain They process information by proces
32、sing large amounts of raw data in a parallel manner,25,Switching System (Adaptive Synapses),Neuron,Neuron,Neuron,Neuron,Neuron,Neuron,Neuron,Neuron,Neuron,26,Neural Networks, contd.,Operations of neural networks Different formulations of neural networks are used to Segment or cluster data, classify
33、data, make predictive models using data A collection of processing units which mimic the basic operations of real neurons is used to perform these functions Learning or training As the neural network learns or is trained, a set of connection weights between the processing units is modified based on
34、the perceived relationship in the data,27,Learning in Neural Networks,Processing Unit (Collection of Neurons),Processing Unit (Collection of Neurons),Processing Unit (Collection of Neurons),Processing Unit (Collection of Neurons),Processing Unit (Collection of Neurons),Connection Weight,Connection W
35、eight,Connection Weight,28,Neural Network and Cognitive Functions,Neural networks Compared to symbol processing systems, neural networks perform relatively low-level cognitive functions Knowledge gain through learning is stored in the connection weights and is not available for examination & manipul
36、ation Adaptability The ability of neural networks to learn from and adapt to their surrounds is a crucial function needed by intelligent software systems Cognition From a cognitive science perspective, neural networks are more like the underlying pattern recognition and sensory processing that is pe
37、rformed by the unconscious levels of the human mind,29,The Internet and the WWW,The Internet grew out of government funding for high energy physics researchers who needed to collaborate over great distances Byproduct of solving the communication problem Developed protocols that allows different comp
38、uters to talk to each other, exchange data, and work together TCP/IP became the de facto standard networking protocol for the Internet Astounding Growth in the Internet Exponential growth in the number of sites Thousands of new sites are connected to the Internet each month,30,The Internet and the W
39、WW, contd.,Internet Services Electronic mail was once the primary service provided by the Net Information publishing and software distribution are now of equal importance The Gopher text information service: early 1990s Gopher was the information publishing on the Net FTP provides valuable services
40、Download research papers and articles, retrieve software updates, and download complete software products It was HTTP that brought the Internet from the realm of academia and computer technologists into the public consciousness,31,The Internet and the WWW,Mosaic browser: University of Illinois Trans
41、formed the Internet into a general-purpose communication medium Computer novices and experts, consumers, and businesses interact in entirely new ways The Net has become a new business platform Web Services The Web publishing and broadcasting capabilities has extended the range of applications and se
42、rvices VoD, streaming audio and video, etc The ubiquitous Web browser provides a universal interface to applications regardless of server platform In the browsing or “pull” mode, the Web allows individual to explore vast amounts of data in a seamless environment,32,Web Services,Limitations of the Br
43、owsing or Pull model The basic problem is that knowing that all the information is out there but not knowing exactly how to find it This can make the Web browsing experience quite frustrating Search engines Search engines and Web index sites such as Alta Vista, Excite, Yahoo, and Lycos provide impor
44、tant services by grouping information by topics and keywords Web browsing is still a hit or miss proposition (with misses more likely than hits),33,Web Services, contd.,Intelligent Agents In the current Web environment, intelligent agents will emerge as truly useful personal assistants Perform tasks
45、 such as searching, finding, and filtering information from the Web, and bringing it to a users attention The Evolving Web The Web is evolving into a “push” or broadcast mode, where users subscribe to sites which send out constant updates to their Web pages In the broadcast mode, the requirement for
46、 filtering information will not go away Unless the broadcast sites are able to send out personalized streams of information,34,Intelligent Agent,Part-III,35,From AI to Intelligent Agents,Whenever a technical field provokes commercial interest, this normally results in intense inertia towards market
47、positioning AI and Commercial Interest The same is true for the AI community There has been a large movement and change of focus in the AI research community to apply the basic artificial intelligence techniques to a host of commercial interest Distributed computer systems, company wide intranets, t
48、he Internet, and the WWW Early focus was on word searches, information retrieval, and filtering tasks,36,From AI to Intelligent Agents, contd.,Intelligent Agents and Commercial Interest Web in evolving into a collaborative commerce (c-commerce) environment transactions are becoming increasing distri
49、buted in nature There significant interest in having smart agents which can perform specific actions Many researchers have turned their focus to looking at how intelligent agents could cooperate to achieve tasks on distributed computer systems There is finally a problem in search of a technology As
50、opposed to the other way around Intelligent Agents can provide real value to users in this new, interconnected, and networked world,37,Summary,Abstract look at software agents We have discussed artificial intelligence and its evolution into software agents at an abstract level We will now take a brief tour of The technical facets of intelligent agents How they work How we classify them based on their abilities and underlying technologies,
