748,927 research outputs found
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Learning Action Models: Qualitative Approach
In dynamic epistemic logic, actions are described using action models. In
this paper we introduce a framework for studying learnability of action models
from observations. We present first results concerning propositional action
models. First we check two basic learnability criteria: finite identifiability
(conclusively inferring the appropriate action model in finite time) and
identifiability in the limit (inconclusive convergence to the right action
model). We show that deterministic actions are finitely identifiable, while
non-deterministic actions require more learning power-they are identifiable in
the limit. We then move on to a particular learning method, which proceeds via
restriction of a space of events within a learning-specific action model. This
way of learning closely resembles the well-known update method from dynamic
epistemic logic. We introduce several different learning methods suited for
finite identifiability of particular types of deterministic actions.Comment: 18 pages, accepted for LORI-V: The Fifth International Conference on
Logic, Rationality and Interaction, October 28-31, 2015, National Taiwan
University, Taipei, Taiwa
The Difficulties of Learning Logic Programs with Cut
As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other hand, searching a space of possible programs (instead of
a space of independent clauses) is unfeasible. An alternative solution is to
generate first a candidate base program which covers the positive examples, and
then make it consistent by inserting cut where appropriate. The problem of
learning programs with cut has not been investigated before and this seems to
be a natural and reasonable approach. We generalize this scheme and investigate
the difficulties that arise. Some of the major shortcomings are actually
caused, in general, by the need for intensional evaluation. As a conclusion,
the analysis of this paper suggests, on precise and technical grounds, that
learning cut is difficult, and current induction techniques should probably be
restricted to purely declarative logic languages.Comment: See http://www.jair.org/ for any accompanying file
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