30,721 research outputs found
RULIE : rule unification for learning information extraction
In this paper we are presenting RULIE (Rule Unification for Learning Information Extraction), an adaptive information extraction algorithm which works by employing a hybrid technique of Rule Learning and Rule Unification in order to extract relevant information from all types of documents which can be found and used in the semantic web. This algorithm combines the techniques of the LP2 and the BWI algorithms for improved performance. In this paper we are also presenting the experimen- tal results of this algorithm and respective details of evaluation. This evaluation compares RULIE to other information extraction algorithms based on their respective performance measurements and in almost all cases RULIE outruns the other algorithms which are namely: LP2 , BWI, RAPIER, SRV and WHISK. This technique would aid current techniques of linked data which would eventually lead to fullier realisation of the semantic web.peer-reviewe
Temporal planning with semantic attachment of non-linear monotonic continuous behaviour
Non-linear continuous change is common in realworld
problems, especially those that model physical
systems. We present an algorithm which builds
upon existent temporal planning techniques based
on linear programming to approximate non-linear
continuous monotonic functions. These are integrated
through a semantic attachment mechanism,
allowing external libraries or functions that are difficult
to model in native PDDL to be evaluated during
the planning process. A new planning system
implementing this algorithm was developed and
evaluated. Results show that the addition of this
algorithm to the planning process can enable it to
solve a broader set of planning problems.peer-reviewe
Explicit versus Latent Concept Models for Cross-Language Information Retrieval
Cimiano P, Schultz A, Sizov S, Sorg P, Staab S. Explicit versus Latent Concept Models for Cross-Language Information Retrieval. In: Boutilier C, ed. IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press; 2009: 1513-1518
Evolving UCT alternatives for general video game playing
We use genetic programming to evolve alternatives
to the UCB1 heuristic used in the standard UCB formulation
of Monte Carlo Tree Search. The fitness
function is the performance of MCTS based on the
evolved equation on playing particular games from
the General Video Game AI framework. Thus, the
evolutionary process aims to create MCTS variants
that perform well on particular games; such variants
could later be chosen among by a hyper-heuristic
game-playing agent. The evolved solutions could
also be analyzed to understand the games better. Our
results show that the heuristic used for node selection
matters greatly to performance, and the vast majority
of heuristics perform very badly; furthermore,
we can evolve heuristics that perform comparably
to UCB1 in several games. The evolved heuristics
differ greatly between games.peer-reviewe
Descriptions as constraints in object-oriented representation
Trabajo presentado al 8th International Joint Conference on Artificial Intelligence (IJCAI) celebrado en Karlsruhe (Alemania) del 8 al 12 de agosto de 1983.A motivation is given to introduce indefinite descriptions. Parts of a description language are presented. Mechanisms for the interpretation of indefinite descriptions are briefly discussed .Peer reviewe
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage
We propose a novel method to merge convolutional neural-nets for the
inference stage. Given two well-trained networks that may have different
architectures that handle different tasks, our method aligns the layers of the
original networks and merges them into a unified model by sharing the
representative codes of weights. The shared weights are further re-trained to
fine-tune the performance of the merged model. The proposed method effectively
produces a compact model that may run original tasks simultaneously on
resource-limited devices. As it preserves the general architectures and
leverages the co-used weights of well-trained networks, a substantial training
overhead can be reduced to shorten the system development time. Experimental
results demonstrate a satisfactory performance and validate the effectiveness
of the method.Comment: To appear in the 27th International Joint Conference on Artificial
Intelligence and the 23rd European Conference on Artificial Intelligence,
2018. (IJCAI-ECAI 2018
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a
robust descriptor of data in the form of persistence diagrams (PDs). PDs
exhibit, however, complex structure and are difficult to integrate in today's
machine learning workflows. This paper introduces persistence bag-of-words: a
novel and stable vectorized representation of PDs that enables the seamless
integration with machine learning. Comprehensive experiments show that the new
representation achieves state-of-the-art performance and beyond in much less
time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on
Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text
overlap with arXiv:1802.0485
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