1. 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

2. 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

3. 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

4. Case representation (table form)

5. 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)

6. 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

7. 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

8. 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.

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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)

15. 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

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