Logicmoo's goal is to enable knowledge engineering capable of modeling the real world in any level of detail. Logicmoo is working to provide the long overdue discussion scaling platform needed by Knowledge Experts for building new systems.
Creating a more customizable personal assistant and better inference engine are a natural byproduct of solving this problem. We will also be using the NomicMu Game in the creation of true AGI.
Task learning from narrative examples
English to Discrete Event Calculus
Dialog Planning with PDDL
We want to bring fresh life into the Artificial Intelligence field by combining several promising logical theories, and integrating a few experimental elements all our own. Our work is based on the Conceptual Dependency Theory of Roger Schank. We arrange these into the Event Calculus of Eric Muller to form the narratives. With Michael Kifer's F-logic we fill in Events Calculus holes. And John McCarthy's elaboration tolerance we have the ability to accept changes to the representation of facts about a subject without having to start all over. Often the addition of a few sentences describing the change suffices for humans and so should also suffice for computer programs. There are many kinds of elaborations a person can tolerate, and they pose different problems to different logical formulations:
analogical planning storing successful plans and adapting them to future problems daydreaming goals strategies for what to think about hierarchical planning achieving a goal by breaking it down into subgoals episode indexing in F-Logic and retrieval mechanisms for indexing and retrieval of cases serendipity detection and application a mechanism for recognizing and exploiting accidental relationships among problems action mutation a strategy for generating new possibilities when the system is stuck
Schank's conceptual dependency theory has laid dormant for want of various technological advancements for so long that most scientists of the field are unaware of its successes and potential. This project is unique in that the elements it seeks to combine are novel and our usage of these elements is quite different than anything previously attempted. Narrative Intelligence has proved itself a useful process but development has halted. Unfortunately the likelihood of educational curriculums producing qualified candidates in the future is low. We believe our method may overcome current limits and show sufficient coverage for understanding which we will be able to build from as a basis for human computer interaction.