Juraj Dzifcak

What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution

Robots that can be given instructions in spoken language need to be able to parse a natural language utterance quickly, determine its meaning, generate a goal representation from it, check whether the new goal conflicts with existing goals, and if acceptable, produce an action sequence to achieve the new goal (ideally being sensitive to the existing goals). In this paper, we describe an integrated robotic architecture that can achieve the above steps by translating natural language instructions incrementally and simultaneously into formal logical goal description and action languages, which can be used both to reason about the achievability of a goal as well as to generate new action scripts to pursue the goal. We demonstrate the implementation of our approach on a robot taking spoken natural language instructions in an office environment.

Towards overcoming the knowledge acquisition bottleneck in answer set prolog applications: embracing natural language inputs

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.