Customisable Multi-Processor Acceleration of Inductive Logic Programming

A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming

This paper describes a customisable processor designed to accelerate execution of inductive logic programming, targeting advanced field-programmable gate array (FPGA) technology. The instruction set and the microarchitecture of the processor are optimised for key operations in logic programming, such as unification and backtracking. Such optimisations reduce external memory access to enable per...

Customising parallelism and caching for machine learning

Inductive logic programming is an attractive and expressive paradigm for machine learning. A drawback of inductive logic programs is their demanding computational requirements. We present an FPGA-based multi-processor architecture aimed at fast execution of such programs. The architecture exploits both coarse-grained parallelism at the query level, and fine-grained parallelism in the unificatio...

Machine Learning and Inductive Logic Programming for Multi-agent Systems

Collaborative Logic Programming via Deductive-inductive Resolution

This thesis presents a powerful deductive-inductive resolution technique, by combining deductive theorem proving with inductive logic programming, for solving a new class of multi-agent problems—namely the collaborative logic programming (CollabLP) problems. In essence, the CollabLP formulation captures a wide range of problems in multi-agent settings where knowledge is typically distributed, p...

Using ILP-Systems for Verification and Validation of Multi-agent Systems

Most applications of inductive logic programming focus on prediction or the discovery of new knowledge. We describe a less common application of ILP namely veriication and validation of knowledge based systems and multi-agent systems. Using inductive logic programming, partial declarative speciications of the software can be induced from the behavior of the software. These rules can be readily ...