In recent years, several frameworks and systems have been proposed that extend Inductive Logic programming (ILP) to the answer set programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis tog...
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In recent years, several frameworks and systems have been proposed that extend Inductive Logic programming (ILP) to the answer set programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples;however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered answersets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.
Management of chronic diseases such as chronic heart failure (CHF) is a major problem in health care. A standard approach followed by the medical community is to have a committee of experts develop guidelines that all...
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Management of chronic diseases such as chronic heart failure (CHF) is a major problem in health care. A standard approach followed by the medical community is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are ignored or not complied with at all. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. For instance, for CHF, the guidelines run nearly eighty pages. In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming (ASP). Our approach is based on developing reasoning templates, that we call knowledge patterns, and using them to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system works even in the presence of incomplete information.
In recent years, several frameworks and systems have been proposed that extend Inductive Logic programming (ILP) to the answer set programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis tog...
详细信息
In recent years, several frameworks and systems have been proposed that extend Inductive Logic programming (ILP) to the answer set programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples;however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered answersets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.
Existing CSP model checkers are incapable of verifying multiple properties concurrently in one run of a model checker, and when trying to alleviate state space explosion problem, most of reduction work are usually don...
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ISBN:
(纸本)9781450348294
Existing CSP model checkers are incapable of verifying multiple properties concurrently in one run of a model checker, and when trying to alleviate state space explosion problem, most of reduction work are usually done after rather than before the complete state space was produced. Thus, A new CSP model checking tool named ACSPChecker was developed based on answer set programming, which is a declarative logic programming paradigm for solving combinational search problems with the feature of completely free of sequential dependencies, to verifying multiple properties concurrently in one run of a model checker. Additionally, It integrated an abstraction method, which could be used to alleviate the state space explosion before the complete state space was produced. Furthermore, a preprocessing technique of properties was proposed to improve the verification efficiency by reducing the expense spending on replicated verification of the same sub formulas. The feasibility and efficiency of ACSPChecker are illustrated by the experiments with a classic concurrency problem - dining philosophers problem.
In this paper, we study the introduction of modal past temporal operators in Temporal Equilibrium Logic (TEL), an hybrid formalism that mixes linear-time modalities and logic programs interpreted under stable models a...
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We first embed Pearce's equilibrium logic and Ferraris's propositional general logic programs in Lin and Shoham's logic of GK, a nonmonotonic modal logic that has been shown to include as special cases bot...
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We first embed Pearce's equilibrium logic and Ferraris's propositional general logic programs in Lin and Shoham's logic of GK, a nonmonotonic modal logic that has been shown to include as special cases both Reiter's default logic in the propositional case and Moore's autoepistemic logic. From this embedding, we obtain a mapping from Ferraris's propositional general logic programs to circumscription, and show that this mapping can be used to check the strong equivalence between two propositional logic programs in classical logic. We also show that Ferraris's propositional general logic programs can be extended to the first-order case, and our mapping from Ferraris's propositional general logic programs to circumscription can be extended to the first-order case as well to provide a semantics for these first-order general logic programs. (C) 2010 Elsevier B.V. All rights reserved.
The DLVHEX system implements the hex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most import...
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The DLVHEX system implements the hex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top of DLVHEX.
Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what...
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ISBN:
(纸本)9781450342322
Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above. The code is available on the *** website.
This paper introduces the agent platform HumanSim, a combination of the BDI-paradigm and answer set programming (ASP), to simulate entities in three-dimensional virtual environments. We show how ASP can be used to (i)...
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ISBN:
(纸本)9783319458892;9783319458885
This paper introduces the agent platform HumanSim, a combination of the BDI-paradigm and answer set programming (ASP), to simulate entities in three-dimensional virtual environments. We show how ASP can be used to (i) annotate a virtual three-dimensional world and (ii) to model the goal selection behavior of a BDI agent. Using this approach it is possible to model the agent domain and its behavior - reactive or foresighted - with ASP. To demonstrate the practical use of HumanSim, we present a three-dimensional planning and simulation application, in which worker agents are driven by HumanSim in the shop floor domain. Furthermore, we show the results of an evaluation of HumanSim in the former mentioned simulation application.
TropICAL is a Domain Specific Language (DSL) for the description of abstract legal policies. Taking inspiration from narrative tropes, our DSL enables the creation of component "policies" that may be reused ...
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ISBN:
(纸本)9781614997269;9781614997252
TropICAL is a Domain Specific Language (DSL) for the description of abstract legal policies. Taking inspiration from narrative tropes, our DSL enables the creation of component "policies" that may be reused between case descriptions. These components are compiled to social institutions, which are realised in answer set programming (ASP) code. In this way, the actions of defendant and plaintiff take the shape of a story which must conform to the rules in the ASP description. We propose the use of our DSL in a tool designed for lawyers to generate arguments for the argumentation process.
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