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Collaborating Authors

Aucher

AAAI Conferences

In epistemic logic, some axioms dealing with the notion of knowledge are rather convoluted and it is difficult to give them an intuitive interpretation, even if some of them, like .2 and .4,



Miura

AAAI Conferences

Axioms can be used to model derived predicates in domain-independent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search spaces than the original model. We propose a method for automatically extracting a particular class of axioms from standard STRIPS PDDL models. More specifically, we identify operators whose effects become irrelevant given some other operator, and generate axioms that capture this relationship. We show that this algorithm can be used to successfully extract axioms from standard IPC benchmark instances, and show that the extracted axioms can be used to significantly improve the performance of satisficing planners.


Miura

AAAI Conferences

Axioms can be used to model derived predicates in domain-independent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search spaces than the original model. We propose a method for automatically extracting a particular class of axioms from standard STRIPS PDDL models. More specifically, we identify operators whose effects become irrelevant given some other operator, and generate axioms that capture this relationship. We show that this algorithm can be used to successfully extract axioms from standard IPC benchmark instances, and show that the extracted axioms can be used to significantly improve the performance of an IP-based planner.


Orfan

AAAI Conferences

We present a framework that learns commonsense temporal knowledge from word definitions. Our work differs from existing systems in both the way definitions are axiomatized and the way knowledge is inferred from those axioms. First, we go beyond axiomatizing just the literal interpretation of a definition by considering the underlying subtext and assumptions a reader has to make to understand a definition. Secondly, we cluster the concept axioms into small event theories that we use to predict the co-occurrence of concepts in simple scenarios. These predictions allow us to identify knowledge derived from the complex interactions among several definitions that would otherwise be ignored. We show that this framework can derive temporal knowledge across several different concept domains. Results are compared to human judgment and demonstrate the effect several features have on evaluation scores.