this article examines modern methods for analysis and dynamic process modeling withthe context of the capabilities, characteristics and advantages of types of multiagent systems. A practical implementation of the “D...
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ISBN:
(纸本)9781665482134
this article examines modern methods for analysis and dynamic process modeling withthe context of the capabilities, characteristics and advantages of types of multiagent systems. A practical implementation of the “Data to Action” model is proposed through the research simulation modeling system “Photovoltaic (PV) to Smart Energy Storage (SES) Grid”, developed by the authors using a Multi-Agent System for simulating the process of generating electricity produced by a PV park, its storage withthe possibility of Time-Shifting and sale of energy.
We can overcome the pessimism in worst-case routing latency analysis of timing-predictable Network-on-Chip (NoC) workloads by single digit factors through the use of a hybrid FPGA-optimized NoC. Timing-predictable FPG...
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ISBN:
(纸本)9781728199023
We can overcome the pessimism in worst-case routing latency analysis of timing-predictable Network-on-Chip (NoC) workloads by single digit factors through the use of a hybrid FPGA-optimized NoC. Timing-predictable FPGA-optimized NoCs such as HopliteBuf integrate stall-free FIFOs that are sized using offline, static analysis of a user-supplied flow pattern and rates. For certain bursty traffic and flow configurations, the static analysis delivers very large, sometimes infeasible, FIFO size bounds and large worst-case latency bounds. Alternatively, backpressure-based NoCs such as HopliteBP can operate with lower latencies for certain bursty flows. However, they suffer from severe pessimism in the analysis due to the effect of pipelining of packets and interleaving of flows at switch ports. As we show in this paper, a hybrid FPGA NoC that seamlessly composes both design styles on a per-switch basis, delivers the best of both worlds with improved feasibility (bounded operation), and tighter latency bounds. We select the NoC switch configuration though a novel evolutionary algorithm based on Maximum Likelihood Estimation (MLE). For synthetic (RANDOM, LOCAL) and real world (SpMV, Graph) workloads, we demonstrate approximate to 2-3x improvements in feasibility, approximate to 1-6.8x in worst-case latency while only requiring LUT cost approximate to 1-1.5x larger than the cheapest HopliteBuf solution. We also deploy and verify our NoC (PL) and MLE framework (PS) on a Pynq-Z1 to adapt and reconfigure NoC switches dynamically.
the proceedings contain 7 papers. the topics discussed include: bi-ISCA: bidirectional inter-sentence contextual attention mechanism for detecting sarcasm in user generated noisy short text;modelling and reasoning for...
the proceedings contain 7 papers. the topics discussed include: bi-ISCA: bidirectional inter-sentence contextual attention mechanism for detecting sarcasm in user generated noisy short text;modelling and reasoning for indirect sensing over discrete-time via Markov logic networks;streaming and learning the personal context;explainable AI: a narrative review at the crossroad of knowledge discovery, knowledge representation and representation learning;intrinsic, dialogic, and impact measures of success for explainable ai;people in the context - an analysis of game-based experimental protocol;and Bartle taxonomy-based game for affective and personality computing research.
the detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as logic Tensor Networks (LTNs), allow the...
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ISBN:
(数字)9783030863401
ISBN:
(纸本)9783030863401;9783030863395
the detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning withthe ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. this architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture.
We introduce two-dimensional logics based on Lukasiewicz and Godel logics to formalize reasoning with graded, incomplete and inconsistent information. the logics are interpreted on matrices, where the common underlyin...
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ISBN:
(纸本)9783030860592;9783030860585
We introduce two-dimensional logics based on Lukasiewicz and Godel logics to formalize reasoning with graded, incomplete and inconsistent information. the logics are interpreted on matrices, where the common underlying structure is the bi-lattice (twisted) product of the [0, 1] interval. the first (resp. second) coordinate encodes the positive (resp. negative) information one has about a statement. We propose constraint tableaux that provide a modular framework to address their completeness and complexity.
We port Dawson and Gore's general framework of deep embeddings of derivability from Isabelle to Coq. By using lists instead of multisets to encode sequents, we enable the encoding of genuinely substructural logics...
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ISBN:
(纸本)9783030860592;9783030860585
We port Dawson and Gore's general framework of deep embeddings of derivability from Isabelle to Coq. By using lists instead of multisets to encode sequents, we enable the encoding of genuinely substructural logics in which some combination of exchange, weakening and contraction are not admissible. We then show how to extend the framework to encode the linear nested sequent calculus LNSKt of Gore and Lellmann for the tense logic Kt and prove cut-elimination and all required proof-theoretic theorems in Coq, based on their pen-and-paper proofs. Finally, we extract the proof of the cut-elimination theorem to obtain a formally verified Haskell program that produces cut-free derivations from those with cut. We believe it is the first published formally verified computer program for eliminating cuts in any proof calculus.
this paper presents a sequent calculus and a dual domain semantics for a theory of definite descriptions in which these expressions are formalised in the context of complete sentences by a binary quantifier I. I forms...
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ISBN:
(纸本)9783030860592;9783030860585
this paper presents a sequent calculus and a dual domain semantics for a theory of definite descriptions in which these expressions are formalised in the context of complete sentences by a binary quantifier I. I forms a formula from two formulas. Ix[F, G] means 'the F is G'. this approach has the advantage of incorporating scope distinctions directly into the notation. Cut elimination is proved for a system of classical positive free logic with I and it is shown to be sound and complete for the semantics. the system has a number of novel features and is briefly compared to the usual approach of formalising 'the F' by a term forming operator. It does not coincide with Hintikka's and Lambert's preferred theories, but the divergence is well-motivated and attractive.
Smart environments enabled by the Internet of things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial...
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ISBN:
(纸本)9781665412520
Smart environments enabled by the Internet of things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not mediate between conflicting goals among different users and/or system administrators, nor across different IoT verticals. In this article, we propose a declarative approach (and its open-source Prolog prototype) to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals across multiple IoT systems.
Bayesian networks are probabilistic graphical models with a wide range of application areas including gene regulatory networks inference, risk analysis and image processing. Learning the structure of a Bayesian networ...
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ISBN:
(纸本)9780999241196
Bayesian networks are probabilistic graphical models with a wide range of application areas including gene regulatory networks inference, risk analysis and image processing. Learning the structure of a Bayesian network (BNSL) from discrete data is known to be an NP-hard task with a superexponential search space of directed acyclic graphs. In this work, we propose a new polynomial time algorithm for discovering a subset of all possible cluster cuts, a greedy algorithm for approximately solving the resulting linear program, and a generalised arc consistency algorithm for the acyclicity constraint. We embed these in the constraint programmingbased branch-and-bound solver CPBayes and show that, despite being suboptimal, they improve performance by orders of magnitude. the resulting solver also compares favourably with GOBNILP, a state-of-the-art solver for the BNSL problem which solves an NP-hard problem to discover each cut and solves the linear program exactly.
We consider the problem of synthesizing goodenough (GE)-strategies for Linear Temporal logic (LTL) over finite traces or LTL (f) for short. the problem of synthesizing GE-strategies for an LTL formula phi over infinit...
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ISBN:
(纸本)9780999241196
We consider the problem of synthesizing goodenough (GE)-strategies for Linear Temporal logic (LTL) over finite traces or LTL (f) for short. the problem of synthesizing GE-strategies for an LTL formula phi over infinite traces reduces to the problem of synthesizing winning strategies for the formula (there exists O phi) double right arrow phi, where O is the set of propositions controlled by the system. We first prove that this reduction does not work for LTL (f) formulas. then we show how to synthesize GE-strategies for LTL (f) formulas via the Good-Enough (GE)synthesis of LTL formulas. Unfortunately, this requires to construct deterministic parity automata on infinite words, which is computationally expensive. We then show how to synthesize GE-strategies for LTL (f) formulas by a reduction to solving games played on deterministic Buchi automata, based on an easier construction of deterministic automata on finite words. We show empirically that our specialized synthesis algorithm for GE-strategies outperforms the algorithms going through GE-synthesis of LTL formulas by orders of magnitude.
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