Generalized Diagnostics with the Non-Axiomatic Reasoning System (NARS) Bill Power, Xiang Li, and Pei Wang Department of Computer and Information Sciences Temple University, USA

Applications of AGI Techniques Two options: To use an AGI system after customization with domain knowledge and tools To use some techniques developed in AGI research in special-purpose systems

Generalized Diagnostics “Diagnostic”: estimating unknown attributes of a case, or explaining its abnormal attributes, according to its known attributes plus the relevant background knowledge “General diagnostic model” (GDM): domain-independent, though restricted to the diagnostic problems

Case representation (table form)

Case representation (logical form) The value Vij of a case Ci at an attribute Aj can be of type Boolean, numerical, or text In Narsese (the formal language of NARS), the former is represented as {Ci} → [Aj], or simply as Ci → Aj, and the latter as ({Ci} × {Vij}) → [Aj]”, or simply as Ci → (Aj / Vij)

Truth-value defined For statement Ci → Aj, its truth-value is represented by a pair <frequency, confidence> in [0, 1] × (0, 1) Frequency = w+ / w, the ratio of positive evidence among all currently available evidence Confidence = w / (w + k), the ratio of current evidence among the total amount after a constant amount arrives

Truth-value interpreted If the truth-value of C → A comes from the comparison of the instances of the two categories, it is randomness If the truth-value of C → A comes from the comparison of the properties of the two categories, it is fuzziness In general, NARS truth-value summarizes multiple types of uncertainty, and cannot be treated by probability theory

Background knowledge and Questions Background knowledge can take various forms, such as Ai → Aj, Ai ↔ Aj, ($x → Ai) ⇒ ($x → Aj), etc. and each has a truth-value. Questions to be answered can be about the truth-value of Ci → Aj, about the attribute value of Ci → (Aj / ?x), or about the explanation of (Ci → ?x) ⇒ (Ci → (Aj / Vij)), etc.

Strong inference rules Each rule has a truth-value function where the confidence of the conclusion has 1 as upper bound Deduction: {Ci → Aj, Aj → Ak} ⊢ Ci → Ak Analogy on attributes: {Ci → Aj, Aj ↔ Ak} ⊢ Ci → Ak Analogy on cases: {Ci ↔ Cj, Cj → Ak} ⊢ Ci → Ak

weak inference rules Each rule has a truth-value function where the confidence of the conclusion has 0.5 as upper bound (when k = 1) Induction: {Ci → Aj, Ci → Ak} ⊢ Aj → Ak Abduction: {Ci → Aj, Ak → Aj} ⊢ Ci → Ak Comparison: {Ci → Ak, Cj → Ak} ⊢ Ci ↔ Cj

Revision and choice Revision: disjoint evidential bases for the same statement can be merged to get higher confidence Choice by confidence: among conflicting answers, choose the one with the highest confidence Choice by expectation: among competing answers, choose the one with the highest e = c (f − 0.5) + 0.5

Inference procedure For each accepted type of question, design a procedure to chain the inference rules in a certain order to use certain data in the case base and the knowledge base The procedure can be considered as an anytime algorithm that builds a path in a graph by incrementally merging sequential or parallel paths

Learning capability Reasoning and learning are basically the same process The derived conclusions are used to revise and update the case base and knowledge base New attributes are created initially as compound attributes, e.g., {Ci → Aj, Ci → Ak} ⊢ Ci → (Aj & Ak) Active learning: asking the user additional information when necessary

Comparison with other techniques Rule-based or model-based systems: uncertainty, learning Machine learning: Multi-strategies (deduction, induction, abduction, analogy, …) Various types of data (cases, domain rules, statistics, …) Any amount of data (one-shot, transfer, …) Anytime and real-time (incremental update) Explainable answers (concept-level)

Comparison with NARS Similarity: Difference: Basic assumption and design principle Logical representation, semantics, inference rules Difference: Only Q&A on declarative knowledge Limited types of acceptable questions and knowledge Application-specific inference procedure

Domains under consideration Medical diagnostics Adaptive online tutoring Trouble shooting in mechanical and electrical devices … …