Rensselaer Department of Cognitive Science Department of Computer Science
Rensselaer Artificial Intelligence and Reasoning (RAIR) Laboratory
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Solomon Solomon

While current Q&A systems are competent and useful with respect to the information they process, they are very limited when compared to a conversation an analyst could have with a human who has read the same information. Solomon, a radically new Q&A system that will transcend the limitations of existing systems by approaching real conversation with real humans.

Solomon is capable of producing rational, justified answers for conceptual, hypothetical, and even open-ended questions related to knowledge bases derived from reading documents. The theoretical approach underlying the system - which, in short, is to model Q&A on a more sophisticated form of human-machine interaction: one in which the machine has the power of cutting-edge machine reasoning technology.

Six distinguishing attributes of Solomon are:

  1. Knowledge Acquisition via Reading
    Solomon acquires knowledge through a process akin to how humans learn by reading, not by shallow text extraction technology. The knowledge Solomon acquires by reading far exceeds the knowledge acquired by current Q&A systems, which cannot extract arbitrarily complex knowledge from text.
  2. Human - Computer Collaboration via Conversation
    Both Solomon and its users are active participants in the question answering process, with each asking and answering questions of the other. Their collaboration is in the form of a dynamic conversation in English wherein the answer to a question depends in part on the prior conversation (the questions previously asked and answered). Solomon's conversational Q&A is not reducible to the decomposition of a single complex query.
  3. Natural Suppositional Reasoning
    Solomon is not limited to simply answering questions of fact. Solomon supports conversation-based suppositional reasoning, i.e., what if... questions that introduce logical and linguistic contexts wherein further conversation is interpreted and evaluated.
  4. Defensible Answers, Rational Justifications, & Intuitive Explanations
    Solomon incorporates sophisticated automated reasoning and model finding. Answers and justifications relate to either counter-examples or defensible arguments (proofs and arguments by deductive, inductive, abductive, or probabilistic means). These answers and justifications are explained in an intuitive fashion, in English, as part of the normal course of conversation.
  5. Unified Reasoning over Visual and Symbolic Knowledge
    Solomon is able to answer questions requiring comprehension of visual as well as symbolic information. It is not that Solomon reduces the visual to the symbolic, for diagrams, pictures, satellite images, movies, maps, etc., all the things that are at the heart of human-level Q&A, are most certainly not symbolic entities. Solomon uses a new family of visual logics, known simply as Vivid, to represent and reason directly over any computable image.
  6. Seamless Integration of Existing Q&A Systems
    Solomon can be integrated on top of other existing databases and Q&A systems as a meta-Q&A system. Solomon extends Attributes 2-5 across various disparate domains and specialized systems through the sound decomposition of proof-theoretic operations into direct model inspections that are then submitted to subordinate systems as yes/no questions of fact.

May 2007 AQUAINT Meeting Materials

RTE DEV 2007 Submission

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Solomon Project Team
  - Selmer Bringsjord
  - Micah Clark