TITLE: Grounding Symbols: Labelling and Resolving Pronoun Resolution with fLIF Neurons SPEAKER: Fawad Jamshed (School of EIS, Middlesex University) ABSTRACT: If a system can represent knowledge symbolically, and ground those symbols in an environment, then it has access to a vast range of data from that environment. The system described in this paper acts in a simple virtual world. It is implemented solely in fatiguing Leaky Integrate and Fire neurons; it views the environment, processes natural language commands, plans and acts. This paper describes how visual representations are labelled, thus gaining associations with symbols. The labelling is done using a Hebbian learning rule in a semi-supervised manner with simultaneous presentation of the word (label) and a corresponding item in the visual field. The paper then shows how these grounded symbols can be useful in reference resolution. The main goal of this research is to develop an agent which can ground symbols to effectively perceive and interact with its surrounding environment.