Showing papers in "Artificial Intelligence in 2018"

TL;DR: The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature, and to discuss of open problems which may form the basis for fruitful future research.

TL;DR: Zhang et al. as mentioned in this paper proposed a deep learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city.

TL;DR: The computational complexity of LARS formulas and programs, their relationship to Linear Temporal Logic (LTL) and the well-known Continuous Query Language (CQL), and its potential as stream reasoning language itself are studied.

TL;DR: In this article, the authors consider voting under metric preferences, where both voters and alternatives are associated with points in a metric space and each voter prefers alternatives that are closer to her to ones that are further away, and the distortion of a voting rule is defined as the worst-case ratio between the performance of an alternative selected by the rule and that of an optimal alternative.

TL;DR: This framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts.

TL;DR: Random backpropagation (RBP) as mentioned in this paper is a variant of the back propagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates.

TL;DR: The main result shows that the complexity of verifying the preferred semantics rises from coNP- to Σ 2 p -completeness when allowing uncertainty about either attacks or arguments, or both.

TL;DR: A two stage stochastic optimization formulation to consider expected future demand is presented and multiple enhancements to solve large scale problems more effectively and efficiently are provided.

TL;DR: The experimental validation provides evidence that DRL implementations show better performances and faster learning times than their centralized counterparts, while using less computational resources.

TL;DR: This paper investigates the theoretical properties of these existing frameworks for learning programs under the answer set semantics and introduces a new notion of generality of a learning framework, which enables a framework to be more general than another in terms of being able to distinguish one ASP hypothesis solution from a set of incorrect ASP programs.

TL;DR: The problem of determining whether a given data integration system discloses a source query to an attacker is formalised and studied, and a number of techniques for analysing logical privacy issues in ontology-based data integration are introduced.

TL;DR: This work adds to the body of results on using deductive database technology such as SQL and datalog in these areas, and provides an expressive formalism for exception handling by overriding in terms of intrinsic complexity.

TL;DR: In this article, the authors consider the problem of selecting a set of items from a predefined set of attributes, where each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have.

TL;DR: A setting where Electric Vehicles can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme is studied, and two non-optimal algorithms are developed, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems.

TL;DR: A major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the “seams” where their influences overlap is considered, which is more than three times the number of layered behaviors in any prior layered learning system.

TL;DR: Recently, it has become possible to recognize charred crumbs of past breads, batters or porridges from typical charred archaeobotanical assemblages as discussed by the authors, which can help map the distribution of cereal cooking practices in time and space.

TL;DR: It is shown that ILP is fixed-parameter tractable when parameterized by the treedepth of the constraint matrix and the maximum absolute value of any coefficient occurring in the ILP instance.

TL;DR: In this paper, an interplay between the distributed knowledge, coalition strategies, and coalition "know-how" strategies is studied, and the main technical result is a sound and complete trimodal logical system that describes the properties of this interplay.

TL;DR: This paper introduces reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources and data streams, and shows that rM CSs do not only generalize mMCSs to dynamic settings, but also capture/extend relevant approaches w.r.t. dynamics in knowledge representation and stream reasoning.

TL;DR: This work presents a new method for taking the actions of others into account that does not require assumptions to be made about the beliefs and preferences of other agents, and draws on utility-based approaches and expresses the reasoning in the form of arguments and objections to enable full integration with value-based practical reasoning.

TL;DR: A novel multi-armed bandit mechanism that learns the qualities of the experts and guarantees a given target accuracy level in a cost optimal way, and derives an upper bound on the number of time steps this algorithm chooses a sub-optimal set, which depends on the target accuracy and true qualities.

TL;DR: In this paper, the extent of women's role working in the informal sector at marine tourism area in Batu Gong Beach of Konawe District, Malaysia, was analyzed using case study approach, and the results indicated that women who work and have income play significant role to support the family finance.

TL;DR: A new model of graph-based visibility for coalition formation games is proposed and tightly characterize the graph structures that allow polynomial-time convergence – it can be achieved if and only if coalition formation is based on complete or star graphs.

TL;DR: The decoupled search hence branches over center transitions only, and maintains the center-compliant paths for each leaf separately, and has exponential separations to all previous search reduction techniques, i.e., examples where it results in exponentially less effort.

TL;DR: It is proved that checking whether there exists a draw in which a player wins is NP-complete, thereby settling an outstanding open problem.

TL;DR: A model of coalition formation where the value depends on a social network and exactly k coalitions must be formed is studied, and it is shown that, when the number of coalitions, k, is fixed and there are not many negative edges, it is possible to find the coalition structure that maximizes the social welfare in polynomial time.

TL;DR: By dealing seamlessly with both discrete distributions and continuous densities within a rich theory of action, this work provides a very general logical specification of how belief should change after acting and sensing in complex noisy domains.

TL;DR: A dedicated planning method is discussed, that generalizes the state-space temporal planning framework, leveraging SMT-based techniques for temporal networks under uncertainty, and a compilation-based method is presented, that reduces any STPUD problem to an ordinary temporal planning problem.

TL;DR: This scenario will formally show that the use of an argumentation framework with recursive attacks to address a trust model in a collaborative open multi-agent system will produce a partially ordered credibility relation; such relation contains the information that can be justified by an argumentations process.

TL;DR: This work provides a formal characterization of acceptability semantics directly on the Attack–Support Argumentation Framework, and formalizes the relationship between the ASAF and other frameworks on which it is inspired: theArgumentation Framework with Recursive Attacks (AFRA) and the Argumentation framework with Necessities (AFN).