Restricted non-monotonicity has been shown beneficial for the projective arceager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information. In this paper, we propose a novel, fully non-monotonic transition system based on the non-projective Covington algorithm. As a non-monotonic system requires exploration of erro...

We present a supervised sequence to sequence transduction model with a hard attention mechanism which combines the more traditional statistical alignment methods with the power of recurrent neural networks. We evaluate the model on the task of morphological inflection generation and show that it provides state of the art results in various setups compared to the previous neural and non-neural a...

The main aim of this paper is to provide a new analysis of licensers of negative polarity items (NPIs). The problems with Fauconnier-Ladusaw's downward entailment analysis have been argued since Linebarger (1980). I will show that there exists a class of weak NPI licensers characterized by non-monotonicity and exclusivity. Weak negation, which is monotone decreasing, has been known to license w...

We study two important properties for the scalability of a replicated system: genuine partial replication (GPR) and snapshot isolation (SI). We prove that these properties are incompatible. To side step this impossibility result, we propose a novel consistency criterion called Non-Monotonic Snapshot Isolation (NMSI). NMSI retains the most important properties of SI: read-only transactions alway...

Today’s models for propagation-based constraint solvers require propagators as implementations of constraints to be at least contracting and monotonic. These models do not comply with reality: today’s constraint programming systems actually use non-monotonic propagators. This paper introduces the first realistic model of constraint propagation by assuming a propagator to be weakly monotonic (co...

Non-monotonic inference is inference that is defeasible: in contrast with deductive inference, the conclusions drawn may be withdrawn in the light of further information, even though all the original premises are retained. Much of our everyday reasoning is like this, and a non-monotonic approach has applications to a number of technical problems in artificial intelligence. Work on formalizing n...

A key problem in reinforcement learning for control with general function approximators (such as deep neural networks and other nonlinear functions) is that, for many algorithms employed in practice, updates to the policy or Q-function may fail to improve performance—or worse, actually cause the policy performance to degrade. Prior work has addressed this for policy iteration by deriving tight ...

Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [10] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given ta...