Heuristics-based causal discovery

Heuristics-based causal discovery: Discovering causal relations through heuristics-based action planning and dynamical search space adjustment

Grauss, Antonie (TU Delft Mechanical, Maritime and Materials Engineering)

Pan, W. (mentor)
Sijs, J. (graduation committee)

Delft University of Technology

Mechanical Engineering

2022-09-02

To operate in open world environments a symbolic Artificial Intelligence (AI) to be able to adapt and incorporate new objects and relations in its Knowledge Base (KB). Symbolic AI use the objects and relations in their KB to navigate the world and create plans. These KB are filled with knowledge in advance, so they can not add new knowledge when encountering novel situations. This thesis presents Heuristics-Based Causal Discovery (HBCD), a method that identifies and labels causal relations and transforms those causal relations into logic statements, which can be inserted into a KB autonomously.

HBCD can operate in a partially-observable environment by performing actions and observing the effects of its actions. The actions are chosen by heuristics, which are modelled after human strategies for causal discovery. The discovered causal relations are tested for the properties necessity and sufficiency. These properties provide information on the completeness of the result, whether there are any missing causal relations. HBCD uses this information to adjust its search space by moving variables in and out of it during the search. If there are no more missing causal relations HBCD stops the search. The method was tested in two simulated environments and the results are promising.

Causal Discovery
Interventions
Partial Observability

http://resolver.tudelft.nl/uuid:83d273d0-f496-421a-a74d-1656a327ec25

Student theses

master thesis

© 2022 Antonie Grauss