Dummy Nodes

Here you can find a description of all the scripts that implement simplified versions of some cognitive nodes. The idea is to have cognitive nodes as barebones as possible to run experiments where we only want to focus on a specific cognitive node variation / algorithm.

Dummy nodes

Python scripts for the implementation of dummy nodes. The dummy nodes avaliable are:

Drive

class dummy_nodes.dummy_drive.DriveDummy(*args: Any, **kwargs: Any)

Bases: Drive

Drive which evaluation is always 1.0.

evaluate(perception=None)

Get expected valuation for a given perception. In this case, the evaluation is always 1.0.

Parameters:

perception (dict) – The given normalized perception. Not used in this case.

Returns:

Evaluation msg with the evaluation value and the timestamp.

Return type:

cognitive_node_interfaces.msg.Evaluation

Goal

class dummy_nodes.dummy_goal.GoalDummy(*args: Any, **kwargs: Any)

Bases: GoalMotiven

Dummy Goal class: A goal that is never rewarded.

calculate_reward(drive_name)

Calculates the reward of the goal based on the evaluation of the Drive node. In this case, the reward is always 0.0.

Parameters:

drive_name (str) – Name of the drive node. Not used in this case.

Returns:

The reward value and the timestamp.

Return type:

tuple

P-Node

class dummy_nodes.dummy_pnodes.ActivatedDummyPNode(*args: Any, **kwargs: Any)

Bases: DummyPNode

Activated Dummy PNode class

calculate_activation(perception=None, activation_list=None)

Always returns an activation of 1.0

Parameters:

• perception (dict) – The perception for which P-Node activation is calculated. It is not used in this case.

• activation_list (list) – The list of activations to be used for the calculation. It is not used in this case.

Returns:

A msg with the activation of the P-Node and its timestamp.

Return type:

cognitive_node_interfaces.msg.Activation

class dummy_nodes.dummy_pnodes.DummyPNode(*args: Any, **kwargs: Any)

Bases: PNode

Activated Dummy PNode class

calculate_activation(perception=None, activation_list=None)

Activation value for the dummy P-Nodes. This method has to be implemented in a subclass.

Parameters:

• perception (dict) – The perception for which P-Node activation is calculated.

• activation_list (list) – The list of activations to be used for the calculation.

Raises:

NotImplementedError – If the method is not implemented in a subclass.

send_pnode_space_callback(request, response)

Callback that sends the space of the P-Node.

Parameters:

• request (cognitive_node_interfaces.srv.SendGoalSpace.Request) – Empty request.

• response (cognitive_node_interfaces.srv.SendGoalSpace.Response) – Response that contains the space of the P-Node. In this case it is empty.

Returns:

Response that contains the space of the P-Node. In this case it is empty.

Return type:

cognitive_node_interfaces.srv.SendGoalSpace.Response

class dummy_nodes.dummy_pnodes.NonActivatedDummyPNode(*args: Any, **kwargs: Any)

Bases: DummyPNode

Activated Dummy PNode class

calculate_activation(perception=None, activation_list=None)

Always returns an activation of 0.0

Parameters:

• perception (dict) – The perception for which P-Node activation is calculated. It is not used in this case.

• activation_list (list) – The list of activations to be used for the calculation. It is not used in this case.

Returns:

A msg with the activation of the P-Node and its timestamp.

Return type:

cognitive_node_interfaces.msg.Activation

class dummy_nodes.dummy_pnodes.RandomDummyPNode(*args: Any, **kwargs: Any)

Bases: DummyPNode

Random Dummy PNode class

calculate_activation(perception=None, activation_list=None)

Return a random activation

Parameters:

• perception (dict) – The perception for which P-Node activation is calculated. It is not used in this case.

• activation_list (list) – The list of activations to be used for the calculation. It is not used in this case.

Returns:

A msg with the activation of the P-Node and its timestamp.

Return type:

cognitive_node_interfaces.msg.Activation