1. Sam: a model-driven development tool*
* Faccin, J., and Ingrid Nunes. "A tool-supported development method for improved BDI plan selection." Engineering Applications of Artificial Intelligence 62 (2017):

2. A learning-based plan selection technique

3. A learning-based plan selection technique
Softgoal is a secondary goal that cannot be fully achieved by a plan, but more or less satisfied according to the actions performed by this plan, e.g. minimise cost or maximise performance when achieving a goal.
Preference is a value between 0 and 1 expressed over an agent's softgoal. Preferences represent the trade- off among softgoals and indicate the importance of a given softgoal to the agent. The greater the importance given to a softgoal, the greater the preference value assigned to it.
Outcome corresponds to an observable plan output. It is a value that can be measured during and/or after the execution of a plan. Usually, an outcome is related to the use of a particular resource within a domain, such as time, fuel, money, among others.
Influence Factor consists of a context attribute that influences plan outcomes. It is a variable that changes according to context, is directly mapped to an agent's belief, and can affect one or more outcomes. The weather condition can influence the time taken to go to work by driving a car, for example.
Optimisation Function defines how an outcome affects a softgoal. It states if the value of an outcome must be maximised or minimised to better satisfy agent's preferences over a given softgoal.
Plan Metadata Element is the element responsible for linking an outcome, its influence factors, a machine learning model/algorithm, an optimisation function and its respective softgoal to a plan.

4. The BDI4JADE framework
BDI4JADE (Nunes at al., 2011) is a framework that impelemts the BDI architecture on top of JADE.
Nunes, I., Lucena, C.J.P.D., Luck, M., BDI4JADE: a BDI layer on top of JADE. In: Proceedings of International Workshop on Programming Multi-Agent Systems ProMAS, 2011, pp. 88–103.

5. Development method
It is a model-driven development method that specifically focuses on the design and implementation activity to create agent with learning capabilities.
Design activity
Graphical notation
Agent modelling
Agent diagram
Agent model file
Plan modelling
Implementation activity

6. Graphical notation

7. Agent modelling

8. Plan modelling Plan modelling process consists of assigning to each
plan metadata element a set of values regarding
(i) how many plan executions are used for collecting data before building the first
prediction model,
(ii) how many plan executions are performed between prediction model updates, and
(iii) the machine learning model used to create such prediction model.
This assignment is performed within the agent model file.
The information provided to the corresponding
agent model file is presented between brackets.

9. Implementation activity

10. Sam: a model-driven development tool

11. The Transportation System Modelled in Sam.

12. The Sorting System Modelled in Sam.

13. A brief installation tutorial
1. Download and install the Eclipse IDE (Oxygen Modeling Tools version recommended). 2. Open Eclipse and access the menu Help > Install New Software Click the "Add..." button, on the right-hand side of the window. 4. In the textfield "Name" type "Sam", in "Location" insert the following address " and press OK. 5. Select the "Agent Modelling" checkbox and proceed with the installation. The IDE will verify the existence of dependencies and will install them if necessary. 6. Restart Eclipse. After installation: To create a new project: 1. New->Project->General->Project 2. Right Click On Project New->Other->Sam->Agent