327 research outputs found
Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently expressive to represent many events yet sufficiently restrictive
to support learning. We then give algorithms, along with lower and upper
complexity bounds, for the subsumption and generalization problems for AMA
formulas. We present a positive-examples--only specific-to-general learning
method based on these algorithms. We also present a polynomial-time--computable
``syntactic'' subsumption test that implies semantic subsumption without being
equivalent to it. A generalization algorithm based on syntactic subsumption can
be used in place of semantic generalization to improve the asymptotic
complexity of the resulting learning algorithm. Finally, we apply this
algorithm to the task of learning relational event definitions from video and
show that it yields definitions that are competitive with hand-coded ones
Aprendizaje organizacional como base de los procesos de cambio
Este capítulo tiene como propósito retomar aspectos esenciales del aprendizaje tanto desde lo organizacional, como desde lo individual, aportados por diversos autores para comprender cómo promover el cambio en las organizaciones de trabajo. Se parte de la importancia de comprender la complejidad del cambio como consecuencia esencialmente de un sistema en el que se aprende y se comparten modelos mentales, creencias, valores, incertidumbres, tecnología, información, etc., y que tiene por visión el concretar objetivos que le permitan a la organización sobrevivir, crecer, desarrollarse y lograr utilidades, a través de innovación, procesos eficientes, liderazgo, desarrollo de personas y una cultura organizacional fortalecida.1a edició
Extreme State Aggregation Beyond MDPs
We consider a Reinforcement Learning setup where an agent interacts with an
environment in observation-reward-action cycles without any (esp.\ MDP)
assumptions on the environment. State aggregation and more generally feature
reinforcement learning is concerned with mapping histories/raw-states to
reduced/aggregated states. The idea behind both is that the resulting reduced
process (approximately) forms a small stationary finite-state MDP, which can
then be efficiently solved or learnt. We considerably generalize existing
aggregation results by showing that even if the reduced process is not an MDP,
the (q-)value functions and (optimal) policies of an associated MDP with same
state-space size solve the original problem, as long as the solution can
approximately be represented as a function of the reduced states. This implies
an upper bound on the required state space size that holds uniformly for all RL
problems. It may also explain why RL algorithms designed for MDPs sometimes
perform well beyond MDPs.Comment: 28 LaTeX pages. 8 Theorem
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
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GPs are from Mars, Administrators are from Venus: The Role of Misaligned Occupational Dispositions in Inhibiting Mandated Role Change
Research on mandated occupational role change focuses on jurisdictional conflict to explain change failure. Our study of the English National Health Service highlights the role of occupational dispositions in shaping how mandated role change is implemented by members of multiple occupational groups. We find that tension stemming from misaligned dispositions may emerge as members of different occupations interact during their role change implementation efforts. Depending on dispositional responses to tension, change may fail as members of the different occupations avoid interactions. This suggests that effective role change can be elusive even in the initial absence of conflicting occupational interests
An iterative decision-making scheme for Markov decision processes and its application to self-adaptive systems
Software is often governed by and thus adapts to phenomena that occur at runtime. Unlike traditional decision problems, where a decision-making model is determined for reasoning, the adaptation logic of such software is concerned with empirical data and is subject to practical constraints. We present an Iterative Decision-Making Scheme (IDMS) that infers both point and interval estimates for the undetermined transition probabilities in a Markov Decision Process (MDP) based on sampled data, and iteratively computes a confidently optimal scheduler from a given finite subset of schedulers. The most important feature of IDMS is the flexibility for adjusting the criterion of confident optimality and the sample size within the iteration, leading to a tradeoff between accuracy, data usage and computational overhead. We apply IDMS to an existing self-adaptation framework Rainbow and conduct a case study using a Rainbow system to demonstrate the flexibility of IDMS
Genome-wide discovery and characterization of maize long non-coding RNAs
BACKGROUND: Long non-coding RNAs (lncRNAs) are transcripts that are 200 bp or longer, do not encode proteins, and potentially play important roles in eukaryotic gene regulation. However, the number, characteristics and expression inheritance pattern of lncRNAs in maize are still largely unknown. RESULTS: By exploiting available public EST databases, maize whole genome sequence annotation and RNA-seq datasets from 30 different experiments, we identified 20,163 putative lncRNAs. Of these lncRNAs, more than 90% are predicted to be the precursors of small RNAs, while 1,704 are considered to be high-confidence lncRNAs. High confidence lncRNAs have an average transcript length of 463 bp and genes encoding them contain fewer exons than annotated genes. By analyzing the expression pattern of these lncRNAs in 13 distinct tissues and 105 maize recombinant inbred lines, we show that more than 50% of the high confidence lncRNAs are expressed in a tissue-specific manner, a result that is supported by epigenetic marks. Intriguingly, the inheritance of lncRNA expression patterns in 105 recombinant inbred lines reveals apparent transgressive segregation, and maize lncRNAs are less affected by cis- than by trans- genetic factors. CONCLUSIONS: We integrate all available transcriptomic datasets to identify a comprehensive set of maize lncRNAs, provide a unique annotation resource of the maize genome and a genome-wide characterization of maize lncRNAs, and explore the genetic control of their expression using expression quantitative trait locus mapping
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