93 research outputs found
Learning Word Representations from Relational Graphs
Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if there are numerous semantic relations between two words, we can
expect some of the attributes of one of the words to be inherited by the other.
Motivated by this close connection between attributes and relations, given a
relational graph in which words are inter- connected via numerous semantic
relations, we propose a method to learn a latent representation for the
individual words. The proposed method considers not only the co-occurrences of
words as done by existing approaches for word representation learning, but also
the semantic relations in which two words co-occur. To evaluate the accuracy of
the word representations learnt using the proposed method, we use the learnt
word representations to solve semantic word analogy problems. Our experimental
results show that it is possible to learn better word representations by using
semantic semantics between words.Comment: AAAI 201
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Jointly Learning to Label Sentences and Tokens
Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size.
Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations.
In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens.
The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations.
Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling
Constrained Pure Nash Equilibria in Polymatrix Games
We study the problem of checking for the existence of constrained pure Nash
equilibria in a subclass of polymatrix games defined on weighted directed
graphs. The payoff of a player is defined as the sum of nonnegative rational
weights on incoming edges from players who picked the same strategy augmented
by a fixed integer bonus for picking a given strategy. These games capture the
idea of coordination within a local neighbourhood in the absence of globally
common strategies. We study the decision problem of checking whether a given
set of strategy choices for a subset of the players is consistent with some
pure Nash equilibrium or, alternatively, with all pure Nash equilibria. We
identify the most natural tractable cases and show NP or coNP-completness of
these problems already for unweighted DAGs.Comment: Extended version of a paper accepted to AAAI1
A Model for Learning Description Logic Ontologies Based on Exact Learning
We investigate the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries posed to an oracle. We consider membership queries of the form “is a tuple a of individuals a certain answer to a data retrieval query q in a given ABox and the unknown target ontology?” and completeness queries of the form “does a hypothesis ontology entail the unknown target ontology?” Given a DL L and a data retrieval query language Q, we study polynomial learnability of ontologies in L using data retrieval queries in Q and provide an almost complete classification for DLs that are fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries. Some results are proved by non-trivial reductions to learning from subsumption examples
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach
Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems
A Semantical Analysis of Second-Order Propositional Modal Logic
International audienceThis paper is aimed as a contribution to the use of formal modal languages in Artificial Intelligence. We introduce a multi-modal version of Second-order Propositional Modal Logic (SOPML), an extension of modal logic with propositional quantification, and illustrate its usefulness as a specification language for knowledge representation as well as temporal and spatial reasoning. Then, we define novel notions of (bi)simulation and prove that these preserve the interpretation of SOPML formulas. Finally, we apply these results to assess the expressive power of SOPML
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs
The Markov Decision Process (MDP) framework is a versatile method for addressing single and multiagent sequential decision making problems. Many exact and approximate solution methods attempt to exploit struc- ture in the problem and are based on value factoriza- tion. Especially multiagent settings (MAS), however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are overly re- stricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of MASs, exploiting a property that can be thought of as ‘anonymous influence’ in the factored MDP. In particular, we show how anonymity can lead to representational and computational efficiencies, both for general variable elimination in a factor graph but also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear pro- gramming to factored MDPs that were previously un- solvable. Our results are shown for a disease control do- main over a graph with 50 nodes that are each connected with up to 15 neighbors
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