DeepAI AI Chat
Log In Sign Up

Towards a Logic-Based Unifying Framework for Computing

by   Robert Kowalski, et al.

In this paper we propose a logic-based, framework inspired by artificial intelligence, but scaled down for practical database and programming applications. Computation in the framework is viewed as the task of generating a sequence of state transitions, with the purpose of making an agent's goals all true. States are represented by sets of atomic sentences (or facts), representing the values of program variables, tuples in a coordination language, facts in relational databases, or Herbrand models. In the model-theoretic semantics, the entire sequence of states and events are combined into a single model-theoretic structure, by associating timestamps with facts and events. But in the operational semantics, facts are updated destructively, without timestamps. We show that the model generated by destructive updates is identical to the model generated by reasoning with facts containing timestamps. We also extend the model with intentional predicates and composite event predicates defined by logic programs containing conditions in first-order logic, which query the current state.


Programming in logic without logic programming

In previous work, we proposed a logic-based framework in which computati...

Extending Coinductive Logic Programming with Co-Facts

We introduce a generalized logic programming paradigm where programs, co...

Explanations as Programs in Probabilistic Logic Programming

The generation of comprehensible explanations is an essential feature of...

Overcoming Misleads In Logic Programs by Redefining Negation

Negation as failure and incomplete information in logic programs have be...

Learning First-Order Rules with Differentiable Logic Program Semantics

Learning first-order logic programs (LPs) from relational facts which yi...

Database Reasoning Over Text

Neural models have shown impressive performance gains in answering queri...