469 research outputs found
The Language of Search
This paper is concerned with a class of algorithms that perform exhaustive
search on propositional knowledge bases. We show that each of these algorithms
defines and generates a propositional language. Specifically, we show that the
trace of a search can be interpreted as a combinational circuit, and a search
algorithm then defines a propositional language consisting of circuits that are
generated across all possible executions of the algorithm. In particular, we
show that several versions of exhaustive DPLL search correspond to such
well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF.
By thus mapping search algorithms to propositional languages, we provide a
uniform and practical framework in which successful search techniques can be
harnessed for compilation of knowledge into various languages of interest, and
a new methodology whereby the power and limitations of search algorithms can be
understood by looking up the tractability and succinctness of the corresponding
propositional languages
Complexity Results and Approximation Strategies for MAP Explanations
MAP is the problem of finding a most probable instantiation of a set of
variables given evidence. MAP has always been perceived to be significantly
harder than the related problems of computing the probability of a variable
instantiation Pr, or the problem of computing the most probable explanation
(MPE). This paper investigates the complexity of MAP in Bayesian networks.
Specifically, we show that MAP is complete for NP^PP and provide further
negative complexity results for algorithms based on variable elimination. We
also show that MAP remains hard even when MPE and Pr become easy. For example,
we show that MAP is NP-complete when the networks are restricted to polytrees,
and even then can not be effectively approximated. Given the difficulty of
computing MAP exactly, and the difficulty of approximating MAP while providing
useful guarantees on the resulting approximation, we investigate best effort
approximations. We introduce a generic MAP approximation framework. We provide
two instantiations of the framework; one for networks which are amenable to
exact inference Pr, and one for networks for which even exact inference is too
hard. This allows MAP approximation on networks that are too complex to even
exactly solve the easier problems, Pr and MPE. Experimental results indicate
that using these approximation algorithms provides much better solutions than
standard techniques, and provide accurate MAP estimates in many cases
Parameterized Compilation Lower Bounds for Restricted CNF-formulas
We show unconditional parameterized lower bounds in the area of knowledge
compilation, more specifically on the size of circuits in decomposable negation
normal form (DNNF) that encode CNF-formulas restricted by several graph width
measures. In particular, we show that
- there are CNF formulas of size and modular incidence treewidth
whose smallest DNNF-encoding has size , and
- there are CNF formulas of size and incidence neighborhood diversity
whose smallest DNNF-encoding has size .
These results complement recent upper bounds for compiling CNF into DNNF and
strengthen---quantitatively and qualitatively---known conditional low\-er
bounds for cliquewidth. Moreover, they show that, unlike for many graph
problems, the parameters considered here behave significantly differently from
treewidth
Engaging in coparenting changes in couple therapy: Two contrasting cases
Following the task analysis method, this study aimed to confirm the relevance of our model of resolving coparenting dissatisfaction to differentiate between two contrasting couples undergoing couple therapy. The model under study described the steps through which couples resolve coparenting issues in couple therapy for parents. Two contrasting couples were selected from a sample of parents undergoing systemic couple therapy. We analyzed videotaped discussions about the couple's coparenting relationship to select one couple whose interaction quality improved after therapy and one couple who worsened. Records of therapy sessions were rated by two independent coders to verify whether the model of coparenting change was present. Results showed that the couple that improved after therapy presented almost all the steps of the model whereas the couple that worsened after therapy presented only two steps. This study supported the relevance of the model and its various components to discriminate between two contrasting cases.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
RankPL: A Qualitative Probabilistic Programming Language
In this paper we introduce RankPL, a modeling language that can be thought of
as a qualitative variant of a probabilistic programming language with a
semantics based on Spohn's ranking theory. Broadly speaking, RankPL can be used
to represent and reason about processes that exhibit uncertainty expressible by
distinguishing "normal" from" surprising" events. RankPL allows (iterated)
revision of rankings over alternative program states and supports various types
of reasoning, including abduction and causal inference. We present the
language, its denotational semantics, and a number of practical examples. We
also discuss an implementation of RankPL that is available for download
Prenatal intuitive coparenting behaviors
Micro-analytic research on intuitive parenting behaviors has shed light on the temporal dynamics of parent and child interactions. Observations have shown that parents possess remarkable implicit communicative abilities allowing them to adapt to the clues infants give and therefore stimulate the development of many of the infants' abilities, such as communication skills. This work focused on observing intuitive parenting behaviors that were synchronized and coordinated between the parents. We call them prenatal intuitive coparenting behaviors and used an observation task - the Prenatal Lausanne Trilogue Play procedure - to observe them. For this task, the parents role-play their first encounter with their future baby, represented by a doll. Two cases from a study on pregnancy after assisted reproductive technology are provided to illustrate how these behaviors manifest themselves. The observations from the first case suggest that expectant parents can offer the baby a coparental framework, whereas the observations from the second case show that opportunities for episodes of prenatal intuitive coparenting can be missed due to certain relationship dynamics.These kinds of observations deepen our knowledge of the prenatal emergence of the coparenting relationship and allow us to hone our strategies for intervening during pregnancy with couples who experience coparenting difficulties. Furthermore, these observations provide a novel and complementary perspective on prenatal intuitive parenting and coparenting behaviors
Prenatal coparenting alliance and marital satisfaction when pregnancy occurs after assisted reproductive technologies or spontaneously
Although the coparenting relationship has been described as key in family dynamics, very few studies have assessed its development during pregnancy after assisted reproductive technology (ART). In this study, the authors compared the prenatal coparenting relationship in 33 couples who conceived through ART with that of 49 couples who conceived spontaneously, and assessed the association between marital satisfaction and the prenatal coparenting alliance. The first-time parents were met during the second trimester of pregnancy. A validated observational task (the Prenatal Lausanne Trilogue Play) was used to assess their prenatal coparenting relationship, and the Dyadic Adjustment Scale was used to evaluate marital satisfaction. No differences were observed in the two groups\u2019 global prenatal coparenting scores, but the ART couples showed less coparental playfulness than those who conceived spontaneously. Marital satisfaction was higher in women who conceived through ART. These data suggest that infertility and its treatment affect the prenatal coparenting and marital relationships in different ways
Runtime Analysis with R2U2: A Tool Exhibition Report
We present R2U2 (Realizable, Responsive, Unobtrusive Unit), a hardware-supported tool and framework for the continuous monitoring of safety-critical and embedded cyber-physical systems. With the widespread advent of autonomous systems such as Unmanned Aerial Systems (UAS), satellites, rovers, and cars, real-time, on-board decision making requires unobtrusive monitoring of properties for safety, performance, security, and system health. R2U2 models combine past-time and future-time Metric Temporal Logic, “mission time” Linear Temporal Logic, probabilistic reasoning with Bayesian Networks, and model-based prognostics. The R2U2 monitoring engine can be instantiated as a hardware solution, running on an FPGA, or as a software component. The FPGA realization enables R2U2 to monitor complex cyber-physical systems without any overhead or instrumentation of the flight software. In this tool exhibition report, we present R2U2 and demonstrate applications on system runtime monitoring, diagnostics, software health management, and security monitoring for a UAS. Our tool demonstration uses a hardware-based processor-in-the-loop “iron-bird” configuration
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