Clone the repository at (https://github.com/maiquelb/jacamo-wesaac2023). Open the folder hands-on/lab1 with the Visual Studio Code (or access it in the terminal). Then, run the project by typing the command below in the terminal:

• Exercise 1.2.1: open the JaCaMo application file (agents_intro.jcm) and the personal assistant agent code (file src/agt/personal_assistant.asl). Read the files and identify the beliefs, goals, and plans. Try to map the program to the observed behavior.

• Exercise 1.2.2: open the mind inspector for the agents bob and marie. Compare the beliefs there with those identified in the program. Are they the same? Are they represented the same way?

• Exercise 1.2.3: observe the plan in lines 13—​14 of the personal assistant agent code (file src/agt/personal_assistant.asl). What is the difference between this plan and the other ones?

• Exercise 1.2.4: type the command below, replacing <agent_id> by the identifier of an agent (either bob or marie). Observe the behaviour of the agent. Open the mind inspector and check whether the belief base of the agent has changed after running this command.

• Exercise 1.3.1: make the agent bob to inform the current date using the plan inform_date. The other agents must not give this information.

• Exercise 1.3.2: add a new personal assistant agent to the system. This agent should greet in portuguese ("Bom dia.").

• Exercise 1.3.3: set the language of bob to japanese without adding any plan. Run the system and observe the output. Handle exceptions if needed.

To run the smart room project, open the folder hands-on/lab2/smart-room with the Visual Studio Code (or access it in the terminal). Then, run the project by typing the command below in the terminal:

You can see how the system reacts to changes in temperature by opening another terminal and executing the command:

replacing the last number 10 by the current temperature of the room.

• Exercise 2.2.1: open the code of the personal_assistant (file src/agt/personal_assistant.asl), read the code and identify the beliefs, goals, and plans. Try to map the program to the observed behavior.

• Exercise 2.2.2: open the mind inspector for agent rc and compare the beliefs there with those identified in the program. Are they the same? Are they represented the same way? Try to distinguish between beliefs supplied by the agent itself (i.e. mental notes) and those obtained by perception.

• Exercise 2.2.3: change the program so that the target temperature is 15.

• Exercise 2.3.1: add a new plan to print the current state of the HVAC.

• Exercise 2.3.2: change the plans of the previous exercise so that when the hvac state is "cooling" it is printed "so cool" and, when the state is "heating" it is printed "so hot".

• Exercise 2.3.3: open the project in folder lab2/e233 and take a look at the personal assistant program. There is a difference in the plan in lines 16-18. Run the application and evaluate the problems of this implementation. Some ideas about how to fix?

• Exercise 2.3.4: open the project in folder lab2/e234 and take a look at the personal assistant program. The target temperature is as an argument of goal keep_temperature. The project, as it is, works. But what happens if a line like !keep_temperature(35) is added in the program? Explain the behavior produced by this change.

• Exercise 1: open the project lab2/e1, read the .jcm file and the program of the two agents, and execute the application. Now change the plan of Bob to:

  +!start
     <- .send(alice, tell, hello);
        .send(alice, tell, hello);
     .

  run the project again and notice the difference. Now change the plan again to

  +!start
     <- .send(alice, signal, hello);
        .send(alice, signal, hello);
     .

  run the project again and notice the difference.

• Exercise 2: open the project lab2/e2, read the .jcm file and the program of the three agents, and execute the application. Use the mind inspector to see the beliefs of the agents (specially Alice). Now change the plan of Alice to:

  +!start
     <- .wait(500);
        .send(karlos, askOne, vl(_), vl(X));
        .println(X).

  run the project again and notice the difference.

• Exercise 3: open the project lab2/e3, read the program of the three agents, and execute the application. List the sequence of exchanged messages and their performatives. Finally, use the mind inspector "link of plans" (in the bottom of the page) to see the plans of Alice.

You can run the project with the following commands:

• Exercise 4: change the list of options offered to the personal assistants.

• Exercise 5: run the voting protocol twice, with two different options and notice possible problems.

• Exercise 6: currently, the id of the conversation is fixed to 1, this may cause problems when counting the votes. Change the program of the room controller so that the identification is an argument for the goal voting.

• Exercise 7: upgrade the previous version so that the conversation id value is incremented each time a voting protocol is executed.

• Exercise 8: add a new personal assistant. Does the application work properly? The rule all_votes_received is hard-coded for 3 participants. How to make it flexible? Think about possible solutions. Hints: see the internal actions .all_names and .df_register.

This lab uses the project at hands-on/lab4/smart-room-vm. The artifact template for our voting machine is defined in the VotingMachine.java class, but the usage interface is not yet fully implemented. Your first task is to complete this implementation. The following sub-tasks will guide you through it, note also the TODO items marked in comments in the Java class.

• Exercise 4.1.1: complete the artifact’s init method by defining an observable property voting_status and setting its value to "closed".

• Exercise 4.1.2: complete the implementation of the open and close operations.

To solve these tasks, you will have to define and work with observable properties. Tips for a quick start:

• you can have a look at the implementation of the HVAC artifact;

• you can check out Example 01 — Artifact definition, creation, and use from CArtAgO by Examples.

The implementation of the voting machine artifact is ready. Now, it is necessary to instantiate this artifact and make it available to the agents.

• Exercise 4.2.1: complete the TODO in smart-room.jcm to create an instance of the voting machine called vote.

• Exercise 4.2.2: run the application, go to http://localhost:3273, check whether room workspace contains the voting artifct, and inspect its observable properties.

• Exercise 4.2.3: complete the TODOs in smart-room.jcm to make all the agents to focus on the voting artifact.

• Exercise 4.2.4: run the application, go to http://localhost:3272 and check whether the observable properties of the voting artifact are listed as beliefs of the agents.

The voting machine is now ready — and the room controller agent is, in fact, already using it. Still, a few bits are missing:

• Exercise 4.3.1: Complete the TODOs defined in room_controller.asl so that agent open a new voting when needed.

• Exercise 4.3.2: Complete the TODOs defined in personal_assistant.asl so that agents use the voting machine and vote for their preferences.

• Exercise 4.3.3: Complete the TODO defined in personal_assistant.asl so that agents shows the current temperature in its console message.

• Exercise 4.3.4: the personal assistant keeps asking the room controller to change the temperature while it does not matches the agent’s preference. Change this behavior so that the personal assistant adjusts its preference when it perceives the voting result. The preference must increase by one if it is lower than the result. Similarly, it must must decrease by one if it is greater than the result.

The agents are happy with their brand new voting machine — and would like to showcase it to other agents via Dweet.io. Luckily, they already have a DweetArtifact artifact template that they can use for this purpose.

Your task is to complete the room_controller.asl agent program with the following steps (see TODOs):

• Exercise 4.4.1: in smart-room.jcm, create an instance of a DweetArtifact called deeet in the room workspace. This artifact requires a string as creation parameter. Use "jacamo-wesaac2023-<your name>".

• Exercise 4.4.2: link the voting machine to the newly created DweetArtifact using the operation linkArtifacts(ArtId1, "port", ArtId2)

  • note 1: the voting machine artifact template already defines an output port named publish-port

  • note 2: to inspect the information shared by the dweet artifact, use the following command: curl https://dweet.io:443/get/dweets/for/jacamo_wesaac23

  • note 2: further examples and documentation on linking artifacts, see Example 08 — Linkability from CArtAgO by Examples

  • note 3: check out CArtAgO by Examples for a complete tour of all features supported for the enviornment dimension.

Note: To change the preference of the agents in running time, use the following command, replacing <agent_id> with the agent name and <pref_temp> with the preferred temperature.

• Exercise 5.1.1: open the project hands-on/lab5/smart-room-org. Compare the organizational specification in src/org/smart_house.xml with the organizational specification shown above. Try to identify groups, roles, goals, missions, role cardinalities, and goal decompositions

• Exercise 5.1.2: there are some elements in the organizational specification shown above that are not present in the the organizational specification in src/org/smart_house.xml. Add these elements to the specification.

• Exercise 5.1.3: execute the application, organization inspector and observe the state of the organization.

• Exercise 5.1.4: execute the application, mind inspector and observe the beliefs of the agents.

• Exercise 5.1.4: compare the code of the personal assistant of this task with the one of the previous Lab4. What are the differences?

• Exercise 5.2.1: change the maximum number of assistant to 2. Execute the application. What is the outcome?

• Exercise 5.2.2: change the order of options_announced and voting_open in the scheme decide_temp. What changes do you observe in the outcome?

• Exercise 5.2.3: parallelize the execution of the announce_options and open_voting. Analyze the result of the new scheme in the organization inspector.

• Exercise 5.2.4: execute the system and observe whether all the agents have voted. If that is not the case, modify the organizational specification so that every agent votes according to its preference.

• Exercise 5.2.5: extend the organizational specification so that the controller is obliged to announce the result of the voting. Change also the room controller code so that it fulfills this obligation.

In the previous task, the organization is created by the application designer using the jcm file. The agents then act within the resulting organization entity. However, in some applications, the agents may need themselves to create or change the organization entity. In this task, the agents create new voting schemes whenever the temperature differs from their preferences.

• Exercise 5.3.1: open the project hands-on/lab5/smart-room-org-2. Compare the smart-room.jcm with the one of the previous task. What is different?

• Exercise 5.3.2: complete the implementation of the personal assistant (line 30) to create a new voting schema.

• Exercise 5.3.3: complete the implementation of the personal assistant (line 32) make the existing group to manage the voting.

• Exercise 5.4.1: in the project hands-on/lab5/smart-room-org, comment the line { include("$moiseJar/asl/org-obedient.asl") } from the room controller. Run the application and observe the result. What has changed? To help you to answer this question, check out the code at https://github.com/moise-lang/moise/blob/master/src/main/resources/asl/org-obedient.asl.

• Exercise 5.4.2: implement a plan in the room_controller agent that displays all fulfilled obligations. Hint: consider the organizational event oblFulfilled/1.

  oblFulfilled(O) : Obligation O was fulfilled

• Exercise 5.4.3: implement a plan for the personal_assistant agents that reacts to the achievement of the organizational goal closing_voting by printing the current temperature. Hint: consider the organizational belief goalState/5.

  goalState(S, G, LC, LA, T) : Goal G, of scheme S, is in state T (possible values for T are waiting, enabled, and satisfied); LC is the list of agents committed to the goal, and LA is the list of agents that have already achieved the goal.

• Exercise 5.4.4: open the project in the folder hands-on/lab5/smart-room-org-3. This project includes a small change that delays the process of committing with missions. This highlights the fact that the agents who are obliged to vote are the only ones who effectively vote. Agents that are permitted to vote do not vote. This can be observed by checking both the agents' outputs in the MAS console and the normative state of the organization in the organization inspector. Change the implementation so that the agents permitted to vote also engage in the voting process. Hints: (i) consider the organizational belief permission(A,R,G,D) (the agent A is permitted to achieve the goal G with the deadline D because the reason R); (ii) use as inspiration the plans to handle obligations available at https://github.com/moise-lang/moise/blob/master/src/main/resources/asl/org-obedient.asl.

• Exercise 5.5.1: open the project hands-on/lab5/smart-room-org. In the code of the personal assistant, remove the latest line of the plan +!vote_done. Run the application and compare the achieved temperature with the votes of the agents. Check what might be wrong.

• Exercise 5.5.2: open the project hands-on/lab5/smart-room-inst. Compare the organization entity definition at smart-room.jcm with the one of the previous task. What is different?

• Exercise 5.5.3: in the current version of the smart-room-inst application, the goal of ending the voting is never achieved even the responsible agent executes the corresponding operation in the voting artifact. Add a new constitutive rule in src/org/smart_house.sai to specify that the execution of the close operation in the voting artifact counts as the the achievement of the voting_closed goal. Use the following template, replacing the elements in <> by the proper identifiers.

  11: count-as done(<scheme>,<goal>,Agent)
      when <artifact operation name> [sai__agent(Agent)].