Selmer Bringsjord Rensselaer AI & Reasoning Lab Department of Cognitive Science (Chair)

1. LbR and Robust Reasoning (Solidification Loop);LbR and Introspection (Solidification Loop);LbR and Knowledge Acquisition Over Text with Diagrammatic Content (Know. Acquisition Loop)Slides for Noah’s Prospective LbR Programrun “Slide Show” for hyperlinksversion ofJuly 8th, 2005 Selmer Bringsjord Rensselaer AI & Reasoning Lab Department of Cognitive Science (Chair) Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA selmer@rpi.edu

2. LbR and Robust Reasoning • Arguably the best machine reasoning system in the world today: Athena, invented by Konstantine Arkoudas • seamlessly integrated with standard first-order ATPs (e.g., Vampire, SPASS, Otter, and whatever is next in this ever-progressing game) • fully programmable (& allows recursive datatypes, polymorphism, etc.) • human control adjustable from zero to full • includes cutting-edge explanation generation in straight English as a problem solving technique • “natural” format used, not simple chaining or resolution: modeled on what is human-readable (see the pdf avalable @ http://www.mm.rpi.edu/SELPAP/ENCYCCOGSCI/bringsjord.encyccogsci.html for an overview of natural-style reasoning) • natural format allows representation and reasoning over uncertain information • allows ideal mix of efficiency and expressivity via sub-sorted multi-sorted logic (e.g., modal logics can be efficiently encoded in MSLsub) • enables model generation abduction • integrated with model builders • already includes more than the features currently being sought for the ISO & ARDA standard, Common Logic! • proved sound and has fully formal semantics, and falls within the class of well-founded Denotational Proof Languages

3. Robust Reasoning & Explanation Generation in English • Using Athena, we can generate fluid English from proofs and arguments, including partial and defective proofs and arguments. • A problem solving technique that we can therefore support in the Solidification Loop is to have the system generate English to communicate the state of its knowledge, and have humans judge on the basis of this English what adjustments and additions need to be made to the system’s knowledge. • Here is a demo and explanation of our seminal approach/R&D in Natural Language Generation: • http://www.cs.rpi.edu/~khemls/marmml/nlg_demo_draft2.mpg

4. Introspection LbR and Robust Reasoning • Arguably the best machine reasoning system in the world today: Athena, invented by Konstantine Arkoudas • seamlessly integrated with standard first-order ATPs (e.g., Vampire, SPASS, Otter, and whatever is next in this ever-progressing game) • fully programmable (& allows recursive datatypes, polymorphism, etc.) • human control adjustable from zero to full • “natural” format used, not simple chaining or resolution: modeled on what is human-readable (see the pdf avalable @ http://www.mm.rpi.edu/SELPAP/ENCYCCOGSCI/bringsjord.encyccogsci.html for an overview of natural-style reasoning) • natural format allows representation and reasoning over uncertain information • allows ideal mix of efficiency and expressivity via sub-sorted multi-sorted logic (e.g., modal logics can be efficiently encoded in MSLsub) • enables model generation abduction • integrated with model builders • already includes more than the features currently being sought for the ISO & ARDA standard, Common Logic! • proved sound and has fully formal semantics, and falls within the class of well-founded Denotational Proof Languages

5. 1. Abductive Introspection Based on Natural Deduction/Argumentation • When reasoning is based not on flat, uninformative forms of reasoning like resolution, but rather on natural deduction and argumentation, the technique of goal analysis can be used. • Goal analysis allows gaps in reasoning to be readily identified, and the required “shape” of these gaps to be pinned down. • Once these gaps have been identified, abductive hypotheses can be ventured as to how to fill them in in order to complete the reasoning in question. • These hypotheses can then be supplied to the Knowledge Acquisition loop.

6. LbR and Introspection • Arguably the best machine reasoning system in the world today: Athena, invented by Konstantine Arkoudas • seamlessly integrated with standard first-order ATPs (e.g., Vampire, SPASS, Otter, and whatever is next in this ever-progressing game) • fully programmable (& allows recursive datatypes, polymorphism, etc.) • human control adjustable from zero to full • “natural” format used, not simple chaining or resolution: modeled on what is human-readable (see the pdf avalable @ http://www.mm.rpi.edu/SELPAP/ENCYCCOGSCI/bringsjord.encyccogsci.html for an overview of natural-style reasoning) • natural format allows representation and reasoning over uncertain information • allows ideal mix of efficiency and expressivity via sub-sorted multi-sorted logic (e.g., modal logics can be efficiently encoded in MSLsub) • enables model generation abduction • integrated with model builders • already includes more than the features currently being sought for the ISO & ARDA standard, Common Logic! • proved sound and has fully formal semantics, and falls within the class of well-founded Denotational Proof Languages

7. 2. A New Form of Abduction: Model Generation Abduction • Suppose that the system is trying to establish some proposition A on the basis of background knowledge  and a particular theory C, but only has some of the knowledge it takes to substantiate A. • Then the following form of abduction can be run semi-automated using Athena as a problem solving technique, because refinement of C can be carried out by the Know. Acquisition loop; i.e., the information said to be possibly missing can be sought by the Know. Acq. loop. • Please note that model finding is an automated process under Athena. It is a way of building a possible scenario.

8. LbR and Introspection • Arguably the best machine reasoning system in the world today: Athena, invented by Konstantine Arkoudas • seamlessly integrated with standard first-order ATPs (e.g., Vampire, SPASS, Otter, and whatever is next in this ever-progressing game) • fully programmable (& allows recursive datatypes, polymorphism, etc.) • human control adjustable from zero to full • “natural” format used, not simple chaining or resolution: modeled on what is human-readable (see the pdf avalable @ http://www.mm.rpi.edu/SELPAP/ENCYCCOGSCI/bringsjord.encyccogsci.html for an overview of natural-style reasoning) • natural format allows representation and reasoning over uncertain information • allows ideal mix of efficiency and expressivity via sub-sorted multi-sorted logic (e.g., modal logics can be efficiently encoded in MSLsub) • enables model generation abduction • integrated with model builders • already includes more than the features currently being sought for the ISO & ARDA standard, Common Logic! • proved sound and has fully formal semantics, and falls within the class of well-founded Denotational Proof Languages

9. 3. MSL-Encoded Epistemic Logic as a Framework for Introspection • The Challenge: • Provide a rigorous framework for managing when and how reasoning fails, and for determining missing knowledge from the failure and drive the acquisition of that missing knowledge. And, do all this rapidly. • That is, a three-part need: • The envisaged framework must know what it knows, and know what it doesn’t know (relative to its goals). • The framework must be able to arrive at a state in which it does have the knowledge it needs. • This must be done in unprecedentedly (such metareasoning has been notoriously slow in the past) rapid fashion. • The solution: Use extension of techniques already validated for AFRL by Bringsjord and Arkoudas, as explained in this paper. • In addition, use techniques for proof and argument diagnosis, already in use in Bringsjord’s ARDA-sponsored Slate system.

10. Knowledge Acquisition & LbR Text Containing Diagrams & Pictures • Arkoudas and Bringsjord have created a new Denotational Proof Language, tentatively called DNDL, for the express purpose of machine reading of text containining diagrams. • Bringsjord’s RAIR Lab also has a team working on image processing, where the images are specifically ones found in math text to be machine read. This team has working systems now that read diagram-rich text directly. • Because it’s not clear that the texts to be read in this LbR program will have diagrams that are to be machine read in earnest, we don’t include here hyperlinks to technical content.