1. 01 -1 Lecture 01 Intelligent Agents TopicsTopics –Definition –Agent Model –Agent Technology –Agent Architecture

2. 01 -2 Definition –A computing entity (real or virtual) that performs user delegated tasks autonomously Characteristics –Delegation –Communication skills: can communicate with other agents –Autonomy: exhibits, as a consequence of the above, an autonomous behaviour –Monitoring: is able to perceive the environment –Actuation: is able to act in the environment –Intelligence: reactive (simple) or cognitive (more complex) and can evolve in the environment Definition

3. 01 -3 Definition Origins:Origins: –Computational intelligence (or AI) Knowledge Representation/ Reasoning theory/ Intentional systems/ Soft computingKnowledge Representation/ Reasoning theory/ Intentional systems/ Soft computing –Software engineering Image and speech processing/ Objects/ Events handling/ Online monitoringImage and speech processing/ Objects/ Events handling/ Online monitoring –Human interface Cognitive engineering/ User modelling/ Intelligent tutoring/ Interactive experimentsCognitive engineering/ User modelling/ Intelligent tutoring/ Interactive experiments

4. 01 -4 Agent Model from User View Task level skills –Information retrieval/ Information filtering/ Information recommendation/ Resource brokering/ Process automation/ Coaching Knowledge –Developer defined/ User specified/ System learned Communication skills –With user HCI/ Customization/ Personality –With other agents Inter-agent communication Languages (ACL/ KIF/ KQML)

5. 01 -5 Benefits of Agents Increase productivity –Automation of repetitive tasks Reduce cognitive overload –Information customization Reduce workload –Recommendation “ Proactive ” assistance –Learning ( “ Proactive ” : p reventive/ early/ in favor of action ) Reduce training cost –Tutoring Reduce on-line work –Mobile agents work off-line

6. 01 -6 Obstacles to Agents Hype and let-down Direct manipulation addiction –Indirect manipulation paradigm shift Traditional business models –New business models’ impact Security –Mobile agents Privacy –Trust

7. 01 -7 Agent Technology Basic concepts –Agent technology is a not a generally structured feature of an application. –It is a pragmatic set of characteristics, supported by various technologies, which extend the functionality or value of the application. An integrated application of a number of technologies A new set of capabilities added to existent applications The capabilities will become standard, expected features of all applications Have strong human-computer interaction aspects Major technologies –Intelligence able to do something –Agency legal to do something

8. 01 -8 Agent Technology Intelligence –Degree of agent behavior in terms of task processing Preferences Reasoning Learning –Major factors Machinery (Engines) –Knowledge representation/ Reasoning/ Learning/ Ontological engineering/ Knowledge management Content –Domain ontology/ Task knowledge & Database/ Context/ User model/ Grammar/

9. 01 -9 Agent Technology Agency –Degree of agent behavior in terms of authority delegation Autonomy Social ability Reactivity (stimulus -> action) “Pro-activeness” –Major factors Access –Interact with data, applications, services, agents, humans/ Networking/ Mobility/ Negotiation Collaboration Security –Authentication/ Certification/ Authority/ Responsibility/ Privacy/ Integrity/ Mutual trust

10. 01 -10 Basic Agent Architecture

11. 01 -11 Example Agent Architecture Open Sesame! – An Example of Learning Agent Architecture Event Monitor Inference Engine Learning Engine Instruction Editor Fact Interpreter Instruction DB Observation DB Event DB Data Events Off-line Operations On-line Operations User

12. 01 -12 Agent Architecture – Open Sesame! Technology Mapping –Machinery Learning engine/ Inference engine/ Fact interpreter –Content Event DB/ Observation DB/ Instruction DB –Access Event monitor/ Instruction editor –Security System logon (authentication)

13. 01 -13 Agent Architecture – Open Sesame! Event DB –Event (and object) ontology –High-level events Event representation –Context Objects (documents) –Attributes Pre-condition (document type) State (old document state) Post-condition (new document state) –Relationships among high-level events ( event “close” to event “save”) to low-level events ( event “close” to “mouse click ” )

14. 01 -14 Agent Architecture – Open Sesame! Event Monitor –Target application monitoring (thru API) –Event recognition and formulation (according to ontology)

15. 01 -15 Agent Architecture – Open Sesame! Learning Engine –Event sequence pattern Define the properties shared in common by a set of event sequences Use user-defined metrics to measure property similarity for clustering –Event sequence learning by clustering Generate event sequences for new events Insert sequences into proper clusters Analyze clusters according to event patterns and similarity metrics to produce facts –changed secondary clusters ( print word doc on myPrinter) –primary clusters ( print word doc ) –subclusters ( print word doc of <1mB on myPrinter ) –Can event sequences be learned by associations mining/ ANN/..?

16. 01 -16 Agent Architecture – Open Sesame! Observation DB –Fact: a logical expression that states a common set of properties shared by sequences in a cluster. DO {print word doc “myDoc”, mail word doc “myDoc”} WHEN {save word doc “myDoc”} –Observations production by analysis on clusters Use complementary clusters to identify prefix (as IF part) Observations are rules waiting for V & V and user confirmation –DO {print, mail} WHEN {save} IF {word doc}

17. 01 -17 Agent Architecture – Open Sesame! Observation DB –Examples of Observations In-context tips/ Shortcuts suggestion Coaching (when the user presses “?”) “Proactive” assistance Customized offer based on learned user preferences Automation offer for repetitive user tasks Suggestions based on what the user is not doing (e.g., password changing) Notification of significant events

18. 01 -18 Agent Architecture – Open Sesame! Fact Interpreter –Whether the new observation fits into the Observation DB? Task redundant/ Task reduction/ Task conflict/ Task self-Conflict/ Rule abstraction/ Rule specialization/ Rule equivalent – How the user likes the new observation? Declines agent offered observation Undoes under some conditions Repeatedly undoes Disapproves under certain conditions Repeatedly disapproves Dislikes agent ’ s interaction preference

19. 01 -19 Agent Architecture – Open Sesame! Rule Editor –Show rules in menus for user conformation or editing –Collect user interaction preferences thru dialogues when a new observation is made, play this sound

20. 01 -20 Agent Architecture – Open Sesame! Instruction DB –Instructions are rules after V & V and user confirmation –Pre-defined rules Inference Engine –Knowledge-based Reasoning –Take actions vs user approval Approval once Approval always Just do it