ARTIGO TÉCNICO Luís Seabra Lopes1, Member, IEEE, António J.S. Teixeira1, Member, IEEE, Marcelo Quinderé2, Mário Rodrigues3 1 Dep. of Electronics, Telecommunications and Informatics, Universidade de Aveiro, Portugal. 2 IEETA, Universidade de Aveiro, Portugal. 3 Escola Superior de Tecnologia e Gestão de Águeda, Portugal
INTEGRATED CAPABILITIES FOR KNOWLEDGE ACQUISITION THROUGH SPOKEN LANGUAGE INTERACTION IN A MOBILE ROBOT ABSTRACT This paper describes an integrated set of capabilities developed to support knowledge acquisition in a mobile robot through spoken language interaction with its users. The robot is a prototype of an intelligent service robot, designed and developed having in mind hosting tasks in a building or event. The main modules involved in knowledge acquisition, here described, are concerned with spoken language understanding (based on a combination of deep and shallow methods), dialogue management and knowledge management. Question answering, supported by the knowledge management module, is based on deductive and inductive inference. The integration of these capabilities and the efficiency of the knowledge acquisition process are experimentally evaluated.
I. INTRODUCTION In recent years, robotics-related technologies have reached a remarkable level of maturity. Service robots can be found in a variety of fields, entertainment robots are popular, humanoid robot hardware and software are making substantial progress, etc. The next step seems to be to develop companion robots, meaning, intelligent service robots capable of performing useful work in close cooperation/interaction with humans [5][6]. It has been argued that this new generation of robots will need to comply with three criteria [29]. First, these robots must be animate, meaning that they should respond to changing conditions in their environment. This requires a close coupling of perception and action. Second, personal robots should be adaptable to different users and different physical environments. One of the basic capabilities in this respect is to be able to make decisions at the task-level. This is a central topic, since future robots are expected to be modular and reconfigurable, from a hardware point of view, and act in unstructured environments, which means that the number of action alternatives at the task-level will increase significantly, with respect to traditional automation-style robots. The adaptable criterion also implies the need for learning / knowledge acquisition capabilities [3]. Learning is important for robots to adapt to new environments, new users and new tasks. Learning should take place both at the sensorimotor level and at the task level. Finally, robots should be accessible, meaning that they should be able to explain their beliefs, motivations and intentions, and, at the same time, they should be easy to command and instruct. In most cases, accessibility will imply the use of spoken language communication, since it is the natural means of communication for human users. In order to meet the animate, adaptable and accessible criteria for intelligent service robots, it is, therefore, necessary to include in their design such basic capabilities as linguistic communication, reasoning, reactivity and learning. “Integrated Intelligence” is an emerging keyword that identifies an approach to building intelligent artificial agents in which the integration
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of all those aspects of intelligence is considered [29]. Recent research works explore a variety of alternative paths, leading to architectures in which different functionalities are combined in different ways [5][6][25] [28][9][12] [15][35]. As mentioned, the adaptable criterion implies the acquisition of knowledge about environments, users and tasks. In the specific case of hosting, which is one of the scenarios envisaged in our project, the robot needs to acquire and provide knowledge to visitors. Such knowledge may include the physical location of people and facilities in a building, the functions of people in an organization, the conducted projects, etc. This paper describes an integrated set of capabilities developed to support knowledge acquisition in a mobile robot through spoken language interaction with its users. Usually, knowledge acquisition (KA) is understood as the process of accumulating semantic structures (in the classical artificial intelligence sense) for computer-based decision-support systems and includes interviews to experts and/or text analysis. Automating KA involves extracting knowledge from natural language interactions and/or from text. Unfortunately, KA from natural language can still be considered a relatively immature field [20]. There is little research on robots with symbolic knowledge processing capabilities. MARCO [16] is an agent capable of following free-form natural language route instructions in a virtual indoor environment. Using linguistic and spatial knowledge, it can infer implicit actions. However, it does not have a KA capability. Given that the scenarios in which our robot must act can be dynamic, it is not viable to encode all the required knowledge. The robot therefore needs KA capabilities. The main sources of such knowledge are the users themselves. In this context, it is necessary to integrate possibly contradictory knowledge from different sources. Related problems have been addressed in other contexts. Benferhat et al. [1] presented strategies for conflict resolution developed to deal with exception handling and iterated belief revision, but which could also be applied in merging information from different sources. A method to construct an integrated knowledge