A weighted causal theory for acquiring and utilizing open knowledge

A weighted causal theory for acquiring and utilizing open knowledge

Motivated by enabling intelligent robots/agents to make use of open-source knowledge resources to solve open-ended tasks, a weighted causal theory is introduced as the formal basis for the development of these robots/agents. The action model of a robot/agent is specified as a causal theory following McCain and Turner’s nonmonotonic causal theories. New knowledge is needed when the robot/agent i...

An Open Repository Model for Acquiring Knowledge About Scientific Experiments

The availability of high-quality metadata is key to facilitating discovery in the large variety of scientific datasets that are increasingly becoming publicly available. However, despite the recent focus on metadata, the diversity of metadata representation formats and the poor support for semantic markup typically result in metadata that are of poor quality. There is a pressing need for a meta...

Understanding User Instructions by Utilizing Open Knowledge for Service Robots

Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., ‘take’, ‘get’, ‘put’). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with...

Humean and an African Causal Theory: A Comparative Study

A General Model for Acquiring Knowledge

In this paper, based on the work in , we further give a general model for acquiring knowledge, which first focuses on the research of how and when things involved in problems are made then describes the goals, the energy and the time to give an optimum model to decide how many related things are supposed to be involved in. Finally, we acquire knowledge from this model in which there are the att...