Intelligent Agents

Intelligent Agents With Java

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

Artificial Intelligence: Introduction • The science 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 promised rapid progress towards practical machines intelligence. Areas of early successes • Game playing, mathematical theorem proving, common-sense reasoning, college mathematics

Introduction, contd. • AI research labs began specializing in narrow fields • Speech recognition • Natural language understanding • 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 but almost impossible for computers to do

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 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

Basic Concepts • AI has always focused on problems which lie just beyond the 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

First Phase • Much of 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 to include sophisticated capabilities relating to reasoning and search techniques

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 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 hazardous work • Automated Process Control (Manufacturing Systems)

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 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

Third Phase • Since the late 1980s much of the AI community has been working on solving 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 control

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 autonomous entities that move through the network, interacting with each other and performing tasks for their users

Intelligent Agents Intelligent agents are software agents that use the latest AI techniques to provide autonomous, intelligent, and mobile software components, thereby extending the reach of users across networks

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 to 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

What do we mean by intelligence? • Do we mean that our agents acts like a human? Think like a human? 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 we would do them • Our agent may not pass the Turing test as a yardstick for judging computer intelligence

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 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

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 more 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

Areas to Explore • Symbol processing • Neural networks • The Internet and the World Wide Web • Events-Conditions-Actions

Intelligent Agents Part-II

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 collects information regarding the patient’s 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 produced by the manipulation of symbols

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 must be represented by a collection of symbols • An appropriate algorithm must then be developed to process these symbols

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 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 that symbols may be appropriate for reasoning and planning • Pattern recognition and learning are suited for other approaches

Manipulation of Symbols • Symbols in the formulations of If-Then rules • Processed using forward and backward chaining reasoning techniques • Forward chaining: system deduce new information 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 in the OO paradigm • Attributes of a concept are grouped together with related procedures for processing

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 represented, and the knowledge itself can be examined and manipulated • Symbol-less approach • An approach that is modeled after the brain

Neural Networks • This technique defines the connectionism camp of artificial intelligence • More focus on how human or natural intelligence occurs • Humans have neural networks, 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 processing large amounts of raw data in a parallel manner

Neuron Neuron Neuron Switching System (Adaptive Synapses) Neuron Neuron Neuron Neuron Neuron Neuron

Neural Networks, contd. • Operations of neural networks • Different formulations of neural networks are used to • Segment or cluster data, classify 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 the perceived relationship in the data

Learning in Neural Networks Processing Unit (Collection of Neurons) Connection Weight Processing Unit (Collection of Neurons) Processing Unit (Collection of Neurons) Connection Weight Processing Unit (Collection of Neurons) Processing Unit (Collection of Neurons) Connection Weight

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 & manipulation • 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 performed by the unconscious levels of the human mind

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 computers 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

The Internet and the WWW, 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 • 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

The Internet and the WWW • Mosaic browser: University of Illinois • Transformed 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 services • 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

Web Services • Limitations of the Browsing 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 important services by grouping information by topics and keywords • Web browsing is still a hit or miss proposition (with misses more likely than hits)

Web Services, contd. • Intelligent Agents • In the current Web environment, intelligent agents will emerge as truly useful personal assistants • Perform tasks 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 filtering information will not go away • Unless the broadcast sites are able to send out personalized streams of information

Intelligent Agent Part-III

From AI to Intelligent Agents • Whenever a technical field provokes commercial interest, this normally results in intense inertia towards market 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, the Internet, and the WWW • Early focus was on word searches, information retrieval, and filtering tasks

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 distributed 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 opposed to the other way around • Intelligent Agents can provide real value to users in this new, interconnected, and networked world

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

Event-Condition-Action • Scenario • Suppose we have an intelligent agent, running autonomously, primed with knowledge about the tasks we required of it. • The agent is ready to move out on the network when the opportunity arises. • Now what? • How does the agent know that we want it to do something for us, or that it should respond to someone who is trying to contact us? • This is where we have to deal with events, recognize conditions, and take actions

Event-Condition-Action Agent If (event1,event2=condition) Then { Action1 Action2 }

Event • Events • An event is anything that happens to change the environment of which the agent should be aware • Arrival of a new piece of mail, change to a Web page, a timer going off at mid-night • Would like to have asynchronous notification of events • Agent would not have to be engaged in busy wait or polling for events • Agents can sleep, think about what has happened during the day, do house keeping tasks, etc, while waiting for the next event • Event notification • When an event occur, the agent has to recognize and evaluate what the event means an then respond to it

Condition-Action • Condition/Recognition • Determining what the condition or state of the world is, could be simple or extremely complex depending on the situation • New mail is a self-describing event • The agent may then query the mail system to find out who sent the mail, what the subject is, or scan the mail text for keywords • All of this is part of the recognition phase • The initial event may wake up the agent, but the agent has to determine the significance of the event in terms of its duties

Condition-Action • Condition/Recognition/Action • If intelligent Agents are going to make our lives easier or more interesting, they must be able to take action, to do things for us • Having an agent take an action for us requires a certain leap of fait or at least some level of trust • We must trust that our intelligent agent is going to behave rationally and in our best interest • Like all situations where we delegate responsibility, we have to weigh the risks and rewards • Risk: agent could mess things up, more work to get it right • Reward: we are free from performing that piece of work

Processing Strategies • Reactive or reflex agents • These are one on the simplest types of agents. They respond in the event-condition-action mode • Reflex agents do not have internal models of the world • They respond solely to external stimuli and the information available from their sensing of the environment • Like neural networks, reactive agents exhibit emergent behavior – interactions of simple individual agents • Reactive agents share low-level data when they interact, not high-level symbolic knowledge • Reactive agents are grounded in physical sensor data and not at the artificial symbolic space • Applications of reactive agents have been limited to robots which use sensors to perceive the world

Processing Strategies • Deliberative or goal-directed agents • These agents have domain knowledge and the planning capability necessary to take a sequence of actions in the hope of reaching or achieving a specific goal • Deliberative agents may proactively cooperate with other agents to achieve a task • They may use any and all of the symbolic artificial intelligence techniques which have been developed over the past forty years

Processing Strategies • Collaborative agents • These agents work together to solve problems • Communication between agents is an extremely important element • Each individual agent is autonomous • The synergy resulting from their cooperation makes them interesting and useful • These agents can solve large problems which are beyond the scope of any single agent and they allow a modular approach based on specialization of agent functions or domain knowledge. • Complex engineering projects – verify different aspects of the design.

Intelligent Agents