This is not all that simple of an article, but it walks you through, from start to finish, how we get from English to logic. In particular, it shows how English sentences can be directly translated into formal logic for use with in automated reasoning with theorem provers, logic programs as simple as Prolog, and even into production rule systems.
There is a section in the middle that is a bit technical about the relationship between full logic and more limited systems (e.g., Prolog or production rule systems). You don’t have to appreciate the details, but we include them to avoid the impression of hand-waving
The examples here are trivial. You can find many and more complex examples throughout Automata’s web site.
Deep natural language understanding (NLU) is different than deep learning, as is deep reasoning. Deep learning facilities deep NLP and will facilitate deeper reasoning, but it’s deep NLP for knowledge acquisition and question answering that seems most critical for general AI. If that’s the case, we might call such general AI, “natural intelligence”.
Deep learning on its own delivers only the most shallow reasoning and embarrasses itself due to its lack of “common sense” (or any knowledge at all, for that matter!). DARPA, the Allen Institute, and deep learning experts have come to their senses about the limits of deep learning with regard to general AI.
General artificial intelligence requires all of it: deep natural language understanding, deep learning, and deep reasoning. The deep aspects are critical but no more so than knowledge (including “common sense”). Continue reading “Natural Intelligence”
In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment. This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education. Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration of the electronic textbook discussed below. There is a video of that presentation here. The introductory remarks are interesting but not pertinent to this article.
Background on Vulcan’s Project Halo
From 2002 to 2004, Vulcan developed a Halo Pilot that could correctly answer between 30% and 50% of the questions on advanced placement (AP) tests in chemistry. The approaches relied on sophisticated approaches to formal knowledge representation and expert knowledge engineering. Of three teams, Cycorp fared the worst and SRI fared the best in this competition. SRI’s system performed at the level of scoring a 3 on the AP, which corresponds to earning course credit at many universities. The consensus view at that time was that achieving a score of 4 on the AP was feasible with limited additional effort. However, the cost per page for this level of performance was roughly $10,000, which needed to be reduced significantly before Vulcan’s objective of a Digital Aristotle could be considered viable.
We show how users can rapidly specify large bodies of deep logical knowledge starting from practically unconstrained natural language text.
English sentences are semi-automatically interpreted into predicate calculus formulas, and logic programs in SILK, an expressive knowledge representation (KR) and reasoning system which tolerates practically inevitable logical inconsistencies arising in large knowledge bases acquired from and maintained by distributed users possessing varying linguistic and semantic skill sets who collaboratively disambiguate grammar, logical quantification and scope, co-references, and word senses.
The resulting logic is generated as Rulelog, a draft standard under W3C Rule Interchange Format’s Framework for Logical Dialects, and relies on SILK’s support for FOL-like formulas, polynomial-time inference, and exceptions to answer questions such as those found in advanced placement exams.
We present a case study in understanding cell biology based on a first-year college level textbook.
The slides for my Business Rules Forum presentation on event semantics and focusing on events in order to simplify process definition and to facilitate more robust governance and compliance are at Event-centric BPM.
After the talk I spoke with Jan Verbeek and Gartjan Grijzen of Be Informed and reviewed their software, which is excellent. They have been quite successful with various government agencies in applying the event-centric methodology to produce goal-driven processing. Their approach is elegant and effective. It clearly demonstrates the merits of an event-centric approach and the power that emerges from understanding event-dependencies. Also, it is very semantic, ontological, and logic-programming oriented in its approach (e.g., they use OWL and a backward-chaining inference engine).
They do not have the top-down knowledge management approach that I advocate nor do they provide the logical verification of governing policies and compliance (i.e., using theorem provers) that I mention in the talk (see Guido Governatori‘s 2010 publications and Travis Breaux‘s research at CMU, for example) but theirs is the best commercially deployed work in separating business process description from procedural implementation that comes to mind. (Note that Ed Barkmeyer of NIST reports some use of SBVR descriptions of manufacturing processes with theorem provers. Some in automotive and aerospace industries have been interested in this approach for quality purposes, too.)
BeInformed is now expanding into the United States with the assistance of Mills Davis and others. Their software is definitely worth consideration and, in my opinion, is more elegant and effective than the generic BPMN approach.
The folks from Knowledge Partners have a post that I found thanks to Sandy Kemsley, whose blog often provides good pointers. This article talks about the decision perspective on business rules. It makes some good points, on which I would like to elaborate albeit at a more semantic or knowledge-level.
Every language has three kinds of statements: questions, statements, and commands. There are also some peripheral types, such as exclamations (Yikes!), but in business processes and decisions only declarative and imperative sentences matter.
From a process- or decision-oriented perspective, decisions are always phrased as imperative sentences. Otherwise, the statements reflected in any business process, whether you are using BPMN or a BRMS, are declarative sentences.
Decisions are imperative sentences because they state an action to be taken. For example, decline a loan or offer a discount. It’s really pretty simple. A decision is an action. Rules that don’t take actions are statements of truth. Such declarative statements of truth are perfect for formal logic, logic programming, and semantic technologies. It’s the action that requires the production rule technology that dominates the market for and applications of rules.
The authors of the aforementioned article use the following diagram to explain the benefits of the decision-oriented approach in simplifying business processes:
The impact is very simple. If you eliminate how you reach decisions from the flow that you diagram in BPMN things get simpler. It’s really as simple as realizing that you have removed all the “if” parts (i.e., the antecedents) of the rule logic from the flow chart.