50,375 research outputs found
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Temporal knowledge graphs (TKGs) model the temporal evolution of events and
have recently attracted increasing attention. Since TKGs are intrinsically
incomplete, it is necessary to reason out missing elements. Although existing
TKG reasoning methods have the ability to predict missing future events, they
fail to generate explicit reasoning paths and lack explainability. As
reinforcement learning (RL) for multi-hop reasoning on traditional knowledge
graphs starts showing superior explainability and performance in recent
advances, it has opened up opportunities for exploring RL techniques on TKG
reasoning. However, the performance of RL-based TKG reasoning methods is
limited due to: (1) lack of ability to capture temporal evolution and semantic
dependence jointly; (2) excessive reliance on manually designed rewards. To
overcome these challenges, we propose an adaptive reinforcement learning model
based on attention mechanism (DREAM) to predict missing elements in the future.
Specifically, the model contains two components: (1) a multi-faceted attention
representation learning method that captures semantic dependence and temporal
evolution jointly; (2) an adaptive RL framework that conducts multi-hop
reasoning by adaptively learning the reward functions. Experimental results
demonstrate DREAM outperforms state-of-the-art models on public datasetComment: 11 page
MCMAS-SLK: A Model Checker for the Verification of Strategy Logic Specifications
We introduce MCMAS-SLK, a BDD-based model checker for the verification of
systems against specifications expressed in a novel, epistemic variant of
strategy logic. We give syntax and semantics of the specification language and
introduce a labelling algorithm for epistemic and strategy logic modalities. We
provide details of the checker which can also be used for synthesising agents'
strategies so that a specification is satisfied by the system. We evaluate the
efficiency of the implementation by discussing the results obtained for the
dining cryptographers protocol and a variant of the cake-cutting problem
A Backward-traversal-based Approach for Symbolic Model Checking of Uniform Strategies for Constrained Reachability
Since the introduction of Alternating-time Temporal Logic (ATL), many logics
have been proposed to reason about different strategic capabilities of the
agents of a system. In particular, some logics have been designed to reason
about the uniform memoryless strategies of such agents. These strategies are
the ones the agents can effectively play by only looking at what they observe
from the current state. ATL_ir can be seen as the core logic to reason about
such uniform strategies. Nevertheless, its model-checking problem is difficult
(it requires a polynomial number of calls to an NP oracle), and practical
algorithms to solve it appeared only recently.
This paper proposes a technique for model checking uniform memoryless
strategies. Existing techniques build the strategies from the states of
interest, such as the initial states, through a forward traversal of the
system. On the other hand, the proposed approach builds the winning strategies
from the target states through a backward traversal, making sure that only
uniform strategies are explored. Nevertheless, building the strategies from the
ground up limits its applicability to constrained reachability objectives only.
This paper describes the approach in details and compares it experimentally
with existing approaches implemented into a BDD-based framework. These
experiments show that the technique is competitive on the cases it can handle.Comment: In Proceedings GandALF 2017, arXiv:1709.0176
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
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