Lecture 01 Intelligent Agents

1. Lecture 01 Intelligent Agents • Topics • Definition • Agent Model • Agent Technology • Agent Architecture

2. Definition • Definition • A computing entity (real or virtual) that performs user delegatedtasks 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

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

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

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. 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. 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. 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. Basic Agent Architecture

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

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

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. 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. 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. 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. 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. 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