Computers surpass humans in many respects, but remain notoriously poor at common sense reasoning, such as reasoning about every day physical objects. In computers this is generally carried out through the use of step-by-step physical simulation; a computer program is given an exact specification of a physical situation, and the computer calculates the precise trajectory of the system at a sequence of discrete time points. There is good reason, however, to think that human beings may use different techniques-- and that the techniques of humans may offer significant advantages over simulation-based calculation. First, humans can make useful qualitative predictions given only partial specifications of the physical and geometrical properties of the situation. Second, humans can make useful qualitative predictions even if they have only a very imperfect knowledge of the physics of the objects or materials involved. Third, humans can predict the qualitative behavior of a complex situation without needing to calculate all the details of the behavior. Finally, humans can use the same physical knowledge for a wide variety of cognitive tasks, including not just prediction but also manipulation, planning design, vision, and text understanding. Our goal is to develop a theory that explains how reasoning with these characteristics can be carried out, both in humans and machines. We aim to study qualitative physical reasoning of this kind from two directions: psychology and artificial

Abstract. Intelligence is a multidimensional problem of which physical reasoning and physical knowledge are important dimensions. However, there are few resources of physical knowledge that can be used in datadriven approaches to Artificial Intelligence. Comirit Objects is a project intended to encourage the general public to contribute to research in Artificial Intelligence by building simple 3D models of everyday objects via an interactive web-site. This paper describes the simplified representation and web-interface used by Comirit Objects and a preliminary investigation into the potential applications of the collected models.