Planeamento

Aulas

Class 1. Theoretical class 1. Presentation

Teachers and corresponding email addresses

Initial notes: computers and software, course website and moodle

Course main subject
Class types and class sequence

Course notes

Evaluation

Doubts and questions


Class 4. Theoretical class 2. Autonomous agents, agent societies, and agent communities

Autonomous agents, agent societies, and agent communities
1. Autonomous agent definition
2. Autonomous agent characterization
3. Usual types / roles of autonomous agents
4. Autonomous agent implementation (languages, tools, platforms)
5. Autonomous agents communities
6. Autonomous agents societies
7. Distributed problem solving in agent societies (general approach)

Two example scenarios
1. Trip buying scenario
2. Machine learning / data science scenario


Class 7. Theoretical class 4. Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers

Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers.


Class 10. Practical class 4 (pencil & paper). Agent communication: questioning, subscription and answering messages

Pencil and paper practical class on agent communication
Main objective: questioning, subscription and answering messages, above all, those of practical classes 3 and 5


Class 13. Theoretical class 8. Agent communication about actions in FIPA ACL and FIPA SL

Agent communication in FIPA ACL and FIPA SL: communication about actiosn
1. ACL messages for talking about actions: request family, failure, refuse, agree, cancel, inform
2. The FIPA request protocol
3. Action terms

4. Message not-understood

Part 2: information requests, inform-if, inform-ref, using agree, refuse, failure


Class 16. Theoretical class 9. Application scenario presentation and description

Application scenario presentation (e.g., go to restaurant in Goa)

Application scenario description

1. Agents and Agent roles
2. Scenario main interactions
3. Scenario preliminary interactions
4. Agent goals
5. Agent actions
6. Agent knowledge
6.1 - Preprogrammed knowledge
6.2 - Information acquired through the interaction

The roles played by agent platforms


Class 19. Theoretical class 12. Inapt Agency: Implementation of the Partner Discovery scenario actions

Inapt Agency: Implementation of the Partner Discovery scenario actions
Part 1.


Class 21. Pratical class 8 (Lab). Implementation and use of the actions of the partner discovery scenario agents

Implementation of the actions of partner discovery scenario agents
Action-based interactions interactions


Class 24. Practical class 9 (Lab). Action preconditions, effects, and impossibilities

Action preconditions, effects, and impossibilities
Blocks world exercises (no agents; just planning)


Class 27. Theoretical class 18. Analysis of the complete partner discovery scenario with planning agents

Show that even though the partner discovery agent receives information about three (N) communities of  book publisher representative agents, it is possible that it considers only one (M, 1 =< M < N) of those representative agents.

This motivates the existence of post-execution waiting conditions.


THIS SUMMARY MAY HAVE TO BE CHANGED
THE SUMMARIES OF CLASSES 28 AND 29 MAY ALSO HAVE TO BE CHANGED


Class 32. Theoretical class 22. A production system to control the partner discovery agent

A production system to control the partner finder agent in the partner discovery scenario.
Create the rules
Use the same actions as the planning based agents



Class 35. Theoretical class 23. Preparation of the evaluation. Second part of the subject matter

Preparation of the evaluation. Second part of the subject matter


Aulas

Class 2. Practical Class 1 (Lab). Inapt Agency Installation

Inapt Agenncy Instalation
1. Virtual Box instalation
2. Downloading of the virtual machine image
3. Configuration of the Autonomous Agents virtual machine
4. Downloading the new version of the inapt.jar; replacement of the inapt.jar on the virtual machine with the new version

HelloWorld agent


Class 5. Theoretical class 3. Agent platform. Inapt Agency

Agent platform: definition (set of services) and practice (set of services + implementation tools)
FIPA Agent platform
Agent platform development
- Platform implementations, FIPA compatibility, Software accessibility (paid vs. freeware / open source)
- Companies
- JADE agent platform

Inapt Agency and the JADE agent platform
Inapt Agency roles and mechanisms with respect to (i) message reception and processing; and (ii) goal directed behavior
Inapt Agents knowledge and actions


Class 8. Theoretical class 5. Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers

Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers.
Part 2


Class 11. Practical class 5 (Lab). Interaction with knowledge-based agents (information subscription)

Interaction with knowledge-based agents with special emphasis on subscription
- Use the Dummy Agent to interact with the knowledge-based agent
- Use a script agent to interact with the knowledge based agent

Example scenario in which the information in the knowledge based agent changes with time, e.g., a crypto quotes agent.


THIS WILL BE CHANGED. A NEW DYNAMIC KNOWLEDGE AGENT WILL BE USED INSTEAD


Class 14. Practical 6 (pencil and paper). Action-based interation

Pencil and paper class on action-based interaction
All request messages and corresponding possible replies (according to the request protocol) in the circular economy scenario


Class 17. Theoretical class 10. Quasi-formal scenario description. An example

Quasi-formal scenario description. An example, e.g., in the circular economy domain
- Agents and agent roles
- Agent interactions
- Agent actions
- Agent knowledge: pre-programmed knowledge and information acquired through the interaction


Class 20. Theoretical class 13. Inapt Agency: Implementation of the Partner Discovery scenario actions

Inapt Agency: Implementation of the Partner Discovery scenario actions
Part 2.


Class 22. Theoretical class 14. Preparation for the evaluation

Preparing the evaluation relative to the first part of the subject matter


Class 25. Theoretical class 16. Action formal descriptions in the partner discovery scenario

Action formal descriptions in the partner discovery scenario: formal description of all actions in the scenario


Class 28. Theoretical class 19. Post-execution waiting conditions

Post-execution waiting conditions
- How to solve the problem of an agent that receives a certain information amount but considers only a part of it? The need for post-execution waiting conditions (n sent questions, n received replies and holding time), statement_response_action specification (w/ conversation id), and action re-implementation
- Specification: post_execution_waiting(Action, ExecutionID)
- Action re-implementation
- statement_response_action

Test the whole scenario again and check that the described approach works well.  This could be an exercise for the students


THIS SUMMARY MAY HAVE TO BE CHANGED
THE SUMMARIES OF CLASSES 27 AND 29 MAY ALSO HAVE TO BE CHANGED


Class 30. Theoretical class 20. Production system: a goal achievement mechanism

Production system: a goal achievement mechanism
- The agent goals in the partner discovery scenario
- Planning based goal achievement and production rule based goal achievement
- Production systems representation (production rules and actions), architecture and functioning

The exemplification of a working production system for a simple scenario (simplified Villain & Hero Game)


Class 33. Practical class 11 (Lab). Additional exercises about production systems

Additional exercises about production systems (no agents; just a knowledge-based system)
Practicla class 11. See the exercise sheet on the discipline web site (http://iscte.pt/~luis/aulas/aa)


Class 36. Theoretical class 24. Preparation of the evaluation. First part of the subject matter

Preparation of the evaluation. First part of the subject matter


Aulas

Class 3. Practical Class 2 (Lab). Agent platform testing

Agent platform testing in the Sound Playing Scenario
See the exercise sheet for practical class 2 in the course website


Class 6. Practical class 3 (Lab). Simple knowledge-based agents

Simple knowledge-based agents
Knowledge-based agents implementation with Inapt Agency
Script "Agents"
Script agents implementation with Inapt Agency

Students scenario
-Testing the knowledge based agent with the Dummy Agent
- Simple agent interaction with a knowledge based agent and a script "agent" (information acquisition)


Class 9. Theoretical class 6. Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers

Agent communication in FIPA-ACL and FIPA-SL. Questions, Subscriptions. Answers.
Part 3


Class 12. Theoretical class 7. Agent communication about actions in FIPA ACL and FIPA SL

Agent communication in FIPA ACL and FIPA SL: communication about actiosn
1. ACL messages for talking about actions: request family, failure, refuse, agree, cancel, inform
2. The FIPA request protocol
3. Action terms

Part 1


Class 15. Practical 7. Laboratory about action-based agent interaction

Laboratory about action-based agent interaction. Examples in the circular economy scenario


Class 18. Theoretical class 11. Quasi-formal description of the partner discovery scenario

Quasi-formal scenario description. An example in the partner discovery scenario
- Agents and agent roles
- Agent interactions
- Agent actions
- Agent knowledge: preprogrammed knowledge and information acquired through interaction


Class 23. Theoretical class 15. Agent goals. Goal achievement mechanisms: planning algorithms

Agent goals. Examples from the partner discovery scenario.
Goal achievement mechanisms (special purpose programs, production rules, search algorithms, planning algorithms)
Goal regression planning algorithm (Ivan Bratko)


Class 26. Theoretical class 17. Definition of the high-level predicates used in the preconditions and effects of the partner discovery scenario

Definition of the  high-level predicates used in the preconditions and effects of the actions of the partner discovery scenario

These high-level predicates are defined in terms of the dynamic low-level scenario predicates. They are needed to avoid using quantifiers


Class 29. Practical class 10 (Lab). Testing the complete partner discovery scenario with planning agents

Testing the complete partner discovery scenario with planning agents
-Tests will be performed both on a local platform and on a remote platform.

- See exercises' sheet number 10 of practical and lab classes' section of the Autonomous Agents' public website (http://iscte.pt/~luis/aulas/aa)

THIS SUMMARY MAY HAVE TO BE CHANGED
THE SUMMARIES OF CLASSES 27 AND 28 MAY ALSO HAVE TO BE CHANGED


Class 31. Theoretical class 21. A production system for a turn changing game

 A production system for a turn changing game
Example of the Villain & Hero game with turn-changing


Class 34. Practical 12 (lab). Testing the complete partner discovery scenario (with a production system)

Testing the complete partner discovery scenario, in which the partner finder agent's goal achievement mechanism is a production rule based system. The other scenario agents are controlled by planning algorithms.

Thus, in the described setting, we are dealing with a rather heterogeneous agent society