# Deliberative and Conceptual Inference in Service Robots
## 1 Inference in Service Robots  
Fully autonomous service robots aimed to support people in common daily tasks require competence in an ample range of faculties, such as perception, language, thought, and motor behavior, in which deployment should be highly coordinated for the execution of service robotics tasks. A hypothetical illustrative scenario in which a general purpose service robot performs as a supermarket assistant is shown in the story-board in Figure 1. The overall purposes of the robot in the present scenario are (i) to attend the customer's information and action requests or commands; (ii) to keep the supermarket in order, and (iii) to keep the manager informed about the state of the inventory in the stands. The basic behavior can be specified schematically; but, if the world varies in unexpected ways, due to spontaneous behavior of other agents or to unexpected natural events, the robot has to reason to complete the service tasks successfully. The robot greets the customer, offers help and the customer asks for a beer in the top-left box 1. The command is performed as an indirect speech act in the form of a question. The robot has conceptual knowledge stored in his knowledge-base, including the obligations and preferences of the agents involved. In this case, the restriction that alcoholic beverages can only be served to people over eighteen. This prompts the robot to issue an information request to confirm the age of the customer. When the user does so, the robot is ready to accomplish the task. The robot has a scheme to deliver the order and also knowledge about the kinds of objects in the supermarket, including their locations. So, if everything is as expected the robot can accomplish the task successfully by executing the scheme. With this information, the robot moves to the stand of drinks where the beer should be found. However, in the present scenario, the Heineken is not there, the scheme is broken down, and, to proceed, the robot needs to reason. As this is an expensive resource, it should be employed on demand. The reasoning process involves three main kinds of inference:  
1. An abductive inference process to the effect of diagnosing the cause of the current state of the world which differs from the expected one. 2. A decision-making inference to the effect of deciding what to do to produce the desired state on the basis of the diagnosis and the overall purposes of the agent in the task. 3. The decision made becomes the goal of a plan that has to be induced and carried out to produce the desired state of the world. We refer to this cycle as the common sense daily-life inference cycle. It also has to be considered that the world may have changed along the development of the task or that the  
Figure 1: Common sense daily-life inference cycle. robot may fail in achieving some actions of the plan, and it needs to check again along the way; if the world is as expected, the execution of the plan is continued; but, if something is wrong, the robot needs to engage recurrently in the daily-life inference cycle until the task is completed, or the robot needs to give up. The first instance of the daily-life inference cycle in the scenario in Figure 1 is shown in box 2. The inference is prompted by the robot's visual behavior, which is goal-directed, that fails to recognize the intended object. This failure is reported through the declarative speech act _I see Malz, but I don't see the Heineken_. Then, the robot performs a diagnosis inference to the effect of determining what went wrong. This kind of reasoning proceeds from an observation to its causes, has an abductive character and is non-monotonic. In this case, the robot hypothesizes where the beer should be and what was the cause of such state (i.e., _Heineken was placed on the shelf of food_).