# Learning and Planning Based on Merged Experience from Multiple Situations for a Service Robot
## 1 Introduction  
Today, an increasing number of robots are developed for service in daily human life, and these robots are expected to be more flexible and efficient. Moreover, robots working in the real world need to adapt to the dynamically changing human-inhabited environments. Thus, approaches to learning the appropriate behavior to acquire task knowledge and deliberation in various situations are essentially required [1]. Early work attempted to address high-level task learning from human demonstration based on experience-based planning domains (EBPDs) [2]. The task models refer to activity schemas composed of action sequences or operation plans and the features of objects involved in the operators. Furthermore, the activity schemas are used for grounding task vocabulary which is transmitted to the robot by the human user, and as a guide to increase the speed of plan searching. However, the approach attempted to abstract activity schema directly from the operators, without a middle layer to learn different action sequences for certain task classes, so it does not support merging the plan-based activity experiences from multiple situations in the same task. Numerous studies have focused on obtaining robot activity experiences and conceptualizing the experiences in Web Ontology Language (OWL) ontology [3; 4; 5]. However, the problem of dynamicallystoring acquired concepts in the robot's ontology has not been addressed to date. The problem of reinterpreting past episodes in terms of a new ontology [4; 6] is a research topic under investigation by several groups; however, a perfect solution has not been developed to date. Generalization is the first step in conceptualizing a plan-based robot activity experience. [PERSON] presents several deductive and inductive transformation methods for the principles that explain the success of a failure-recovery strategy from experienced episodes [7; 8]. Recently, conceptualizing activity experiences in the form of activity schemata that can be used to guide a planner towards a solution has attracted much attention from academia [7; 9]. Following the idea, [PERSON] proposed a conceptualization approach based on deductive generalization, abstraction and feature extraction [10]. Then, a goal inference procedure is added to activity schemas, and the inferred goal is useful in solving different instances of the same problems [2]. In one study [11], the approach is extended to robot experiences containing loops. However, the learned activity schemas are fixed and separated from each other, which causes the difficulty of switching to activity schema when the current learned activity schema fails. Hierarchical task network (HTN) planning is an approach that is adopted extensively for automated planning. Numerous studies focus on learning the applicability conditions from given structural traces of HTNs. Early studies, such as those utilizing the CaMeL algorithm [12] and DInCAD algorithm [13], assumed the complete observability of the intermediate states of the plans and a complete action model was given. Approaches such as the Icarus algorithm [14] and the HTN-MAKER algorithm [15] have been developed to learn the decomposition methods for HTN. It is difficult to apply such approaches because the observed plan traces lack complete and consistent information in real world situations. [PERSON] presented HTN-learner to learn the HTN method preconditions in HTN planning given a set of partially observed decomposition trees [16]. This method is inefficient for scenarios with complex tasks and uncertain environments, in which the number of predicates needed to be identified is large. A plan solution guided by the activity schema is an effective means that uses the learned task knowledge. Schema-Based Planner (SBP), an adapted A* heuristic search planner, is proposed to exploit learned activity schemas as heuristics to make plans in similar situations in experience-based planning domains [17], and it is applied to solving problem instances of the same task with varying sets of objects [11].