In this paper, we present a synopsis of the PlayGround project. Through neural-symbolic learning and reasoning, the PlayGround project assumes that high-level concepts and reasoning processes can be used to advance both symbol grounding and object affordance inference. However, a prerequisite for reasoning about objects and their affordances is integrated object representations that concurrently maintain symbolic values (e.g., high-level concepts), and sub-symbolic features (e.g., spatial aspects of objects). Integrated representations that, preferably, should be based upon neural-symbolic computation such that neural-symbolic models can, subsequently, be used for high-level reasoning processes. Nevertheless, reasoning processes for symbol grounding and affordance inference often require multiple inference steps. Taking inspiration from the cognitive prospects in simulation semantics, the PlayGround project further presumes that these reasoning processes can be simulated by neural rendering complementary to high-level reasoning processes.