As a novel system description and problem solving method,agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent *** research about agent organization are mostl...
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As a novel system description and problem solving method,agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent *** research about agent organization are mostly being undertaken in agent organization model,organization structure,organization rules, organization formation and evolution,so it is necessary to extend the research to analyze mental states and their *** this paper,the mental states of commitments in agent organization are defined and analyzed including internal commitment,social commitment,Group commitment and organization *** semantics and properties of different commitments are given so that advances the works associated with agent organization.
As a novel system description and problem solving method, agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent interactions. Current research about agent org...
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As a novel system description and problem solving method, agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent interactions. Current research about agent organization are mostly being undertaken in an agent organization model, organization structure, organization rules, organization formation and evolution, so it is necessary to extend the research to analyze mental states and their relations. In the paper, the mental states of commitments in agent organization are defined and analyzed including internal commitment, social commitment, group commitment and organization commitment. The semantics and properties of different commitments are given so advancing the works associated with agent organization.
In an attempt to propose a robust method for understanding natural language (NL) interface commands, a scheme is proposed that infers intentions from an indirect speech-act that does not express users' real intent...
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In an attempt to propose a robust method for understanding natural language (NL) interface commands, a scheme is proposed that infers intentions from an indirect speech-act that does not express users' real intentions explicitly. This method classifies the real intentions of the indirect speech-act into: 1) refusal;2) reversal;3) restriction;4) benefit;and 5) disability. Further, concepts are abstracted for operations, e.g., displaying, moving, and deleting information systems;and constructing the operation knowledge base. This knowledge based comprises operational concepts and the relationships between them. These relationships are assigned the foregoing classifications for intentions. In addition, we construct the knowledge base of objects for the target of operations, e.g., files, figures, strings. This knowledge base contains the relationships: a) antonym;b) exclusive;c) part-of, between the objects;and uses these relations to infer the transitions between the objects. An algorithm is the proposed to infer concepts for operations and concepts for target objects of operations that may represent the user's actual intentions. This proposal scheme was tested with requests on UNIX and a commercially available Japanese Word Processor. The system successfully inferred the intentions for approximately 80 percent of the user's indirect speech-act.
Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capa...
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Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.
In this paper, we present a novel indirect convergent Jacobi spectral collocation method for fractional optimal control problems governed by a dynamical system including both classical derivative and Caputo fractional...
Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SS...
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Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://***/ADMIS-TONGJI/RGCN-PSA/.
In this paper, we develop spectral collocation method for a class of fractional diffusion differential equations. Since the solutions of these fractional differential equations usually exhibit singularities at the end...
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