# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Ontology Scope
#### 3.5.3 Task and Action  
KnowRob [99] defines _Action_ as an _Event_ using the definitions of the OpenCyc ontology. The KnowRob [68] knowledge base system provides the basis for the autonomous mobile robot by representing general class knowledge in the form of the ontology for task execution. It uses the well-structured foundational ontology, called DUL, to execute a task by following the \"Plan\" definition of the DUL ontology and defines many activity-related concepts in the Open-EASE ontology. OROUS [69] contains sub-actions composed of the task. For instance, sub-actions _USimagePerception_ and _RoboticBoneTracking_ are performed in _OperatingRoom_. It also defines actions related to medical perception and algorithms and pre- and intra-operative actions in the _MedicalSensing_ and _manipulationAction_ classes. However, the _Action_ definition in natural language is described in a few analyzed approaches (e.g., CARESS [70]). PMK [71] defines the _Action_ class in which symbolic tasks are described at three levels (Task): Level 2 (Sub-Task) includes short-term sequences of _move_, _pickup_, and _place_, while Level 1 contains _AutomicFunction_ for perception and planning processes.  
SARBot [72] uses the _TaskOntology_ for search and rescue operations in which the task contains a subclass of the subtasks and atomic actions; for instance, _SearchTask_, _RescueTask_, and _RecognizeTask_ are three tasks of _TaskOntology_ in which _RescueTask_ has three atomic actions: _PickUpVictim_, _GetOutVictim_, and _PutDownVictim_. In IEQ [73], the _recommendation_ ontology models the concepts and relationship for IEQ evaluation based on comfort standards and helps the robot to complete its task by giving suitable recommendations to the user. The SmartRules [74] abstracts the information from lower level visual data into high-level entities for performing context-aware actions. It proposes _VisualMessage_, _MoveRobot_, and _EmergencyAlarm_ to model the actions in the case of context change. The _Action_ ontology of ARBI [75] expresses the robot's actions, e.g., _moving_, _grabbing_, and _speaking_ in service situations and inter-relations with other models for generating complex knowledge that assists the robot in action selection and event management. The actionontology also contains an _event_ class that stores both recordable, as well as cognitive events and external recognized data in the form of event instances  
Worker-cobot [76] uses the _agent_ ontology, which is divided into three categories to perform a task. The agent _structural_ ontology expresses the relations (i.e., predicates) between the entities of HCA and facilitates better understanding, while the agent _administrative_ ontology represents the operations or actions performed by the HCA entities (i.e., pump assembly, pick and place). We found only the name of ontologies related to tasks and actions, but the description of their classes was not given in [76]. APRS [77] presents an _action_ ontology for all the concepts required to support an action. It contains information for the automatic generation of the PDDL domain file and problem files required by the planner. The _capability_ ontology represents the information related to the robot's capability such as performing assembly actions (i.e., pick-up-part, small-gear).