## 1 Introduction  
An intelligent service robot is a machine that is able to perceive the environment and use its knowledge to operate safely in a meaningful and purposive manner [1]. Intelligent service robots are being developed as a solution for the widening gap between supply and demand of human caregivers for elderly/disabled people [2; 3; 4; 5]. These service robots are intended to be operated by non-expert users in human-populated environments. Thus, human-friendly interactive features are preferred for domestic service robots [6]. Verbal communication is one of the most widely used communication modalities by humans in order to communicate with companions. Therefore, human-like verbal communication abilities are favored for domestic service robots with human- friendly interactive features [7; 8]. The natural verbal communications phrases and utterances that indicate the distances often include uncertain terms such as \"little\" and \"far\". These uncertain terms are sometimes referred to as fuzzy linguistic information, qualitative terms and fuzzy predicates. The quantitative meaning of uncertain information depends on various factors such as environment, context and experience. As an example, consider a situation where a person is standing in front of wall with 5 m gap between the wall and the person. In this case, the person may move about 1-1.5 m for the command, \"move little forward\". However, if the gap between the person and the wall is 1 m then the response of that person for the same command would be a movement of 20-30 cm. Therefore, service robot must possess human-like cognitive ability in understanding uncertain information in order to provide better interaction and service for the human users. Methods for understanding natural language commands related to object manipulations and navigation have been developed and the systems are capable of understanding and successfully executing the robot actions required for fulfilling natural language commands [9; 10]. However, the systems are not effective in quantifying the meaning of uncertain information in user instructions such as \"little\" and \"far\". The system proposed in [11] is capable of generating natural language spatial descriptors that include uncertain terms. However, the quantitative meanings of uncertain terms are fixed. Methods based on fuzzy inference systems for quantifying predetermined values for the uncertain terms in verbal instruction based on the current state of the robot have been proposed [12; 13]. The method proposed in [14] adapts the meaning of uncertain information based on the immediate previous state of the robot. The method proposed in [15] evaluates a set of previous states instead of the immediate previous state for enhanced interpretation. The methods proposed in [16; 17] use fuzzy neural networks that are capable of adapting the perception of uncertain information based on the user critics. However, these systems do not consider environmental factors for adaptation in a manner similar to humans, and they cannot adapt perception according to the environment even though the fuzzy implications related to the spatial information heavily depend on the environment. A method for manipulating objects through voice instructions with fuzzy predicates has been developed [18]. The method is capable of evaluating crisp values for fuzzy predicates in user instructions by evaluating the average distance between surrounding objects in its vision field. A concept to scale the fuzzy fluent related to positional information based on the size of the frame/point of view has been introduced in [19]. However, according to [20], the size of the frame (e.g., size of the room) is not enough for adapting the perception of uncertain information related to navigational commands. Hence, the method considers more environmental factors for the adaptation. Moreover, the proposed method evaluates the arrangement of the surrounding environment in a more rational way in order to adapt the perception of uncertain information.