The Experts below are selected from a list of 41025 Experts worldwide ranked by ideXlab platform

Marco Riani - One of the best experts on this subject based on the ideXlab platform.

  • Robust Bayesian regression with the Forward Search: theory and data analysis
    TEST, 2017
    Co-Authors: Anthony C. Atkinson, Aldo Corbellini, Marco Riani
    Abstract:

    The frequentist Forward Search yields a flexible and informative form of robust regression. The device of fictitious observations provides a natural way to include prior information in the Search. However, this extension is not straightForward, requiring weighted regression. Bayesian versions of Forward plots are used to exhibit the presence of multiple outliers in a data set from banking with 1903 observations and nine explanatory variables which shows, in this case, the clear advantages from including prior information in the Forward Search. Use of observation weights from frequentist robust regression is shown to provide a simple general method for robust Bayesian regression.

  • Cluster detection and clustering with random start Forward Searches
    Journal of Applied Statistics, 2017
    Co-Authors: Anthony C. Atkinson, Marco Riani, Andrea Cerioli
    Abstract:

    The Forward Search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the Forward Search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the Forward Search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with 8 clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.

  • Introducing Prior Information into the Forward Search for Regression
    Topics on Methodological and Applied Statistical Inference, 2016
    Co-Authors: Anthony C. Atkinson, Aldo Corbellini, Marco Riani
    Abstract:

    The Forward Search provides a flexible and informative form of robust regression. We describe the introduction of prior information into the regression model used in the Search through the device of fictitious observations. The extension to the Forward Search is not entirely straightForward, requiring weighted regression. Forward plots are used to exhibit the effect of correct and incorrect prior information on inferences.

  • The Forward Search for Very Large Datasets
    Journal of Statistical Software, 2015
    Co-Authors: Marco Riani, Domenico Perrotta, Andrea Cerioli
    Abstract:

    The identification of atypical observations and the immunization of data analysis against both outliers and failures of modeling are important aspects of modern statistics. The Forward Search is a graphics rich approach that leads to the formal detection of outliers and to the detection of model inadequacy combined with suggestions for model enhancement. The key idea is to monitor quantities of interest, such as parameter estimates and test statistics, as the model is fitted to data subsets of increasing size. In this paper we propose some computational improvements of the Forward Search algorithm and we provide a recursive implementation of the procedure which exploits the information of the previous step. The output is a set of efficient routines for fast updating of the model parameter estimates, which do not require any data sorting, and fast computation of likelihood contributions, which do not require matrix inversion or qr decomposition. It is shown that the new algorithms enable a reduction of the computation time by more than 80%. Furthemore, the running time now increases almost linearly with the sample size. All the routines described in this paper are included in the FSDA toolbox for MATLAB which is freely downloadable from the internet.

  • Strong consistency and robustness of the Forward Search estimator of multivariate location and scatter
    Journal of Multivariate Analysis, 2014
    Co-Authors: Andrea Cerioli, Alessio Farcomeni, Marco Riani
    Abstract:

    The Forward Search is a powerful general method for detecting anomalies in structured data, whose diagnostic power has been shown in many statistical contexts. However, despite the wealth of empirical evidence in favor of the method, only few theoretical properties have been established regarding the resulting estimators. We show that the Forward Search estimators are strongly consistent at the multivariate normal model. We also obtain their finite sample breakdown point. Our results put the Forward Search approach for multivariate data on a solid statistical ground, which formally motivates its use in robust applied statistics. Furthermore, they allow us to compare the Forward Search estimators with other well known multivariate high-breakdown techniques.

Ronen I. Brafman - One of the best experts on this subject based on the ideXlab platform.

  • Distributed heuristic Forward Search for multi-agent planning
    Journal of Artificial Intelligence Research, 2014
    Co-Authors: Raz Nissim, Ronen I. Brafman
    Abstract:

    This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of prob- lem - one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic Forward Search. The question arises whether it is possible to formulate a distributed heuristic Forward Search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space Search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient - outperforming previous algorithms by orders of magnitude - while offering similar flexibility to that of Forward-Search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the a* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.

  • A Forward Search Planning Algorith with a Goal Ordering Heuristic
    2014
    Co-Authors: Igor Razgon, Ronen I. Brafman
    Abstract:

    Forward chaining is a popular strategy for solving classical planning problems and a number of recent successful planners exploit it. To succeed, a Forward chaining algorithm must carefully select its next action. In this paper, we introduce a Forward chaining algorithm that selects its next action using heuristics that combine backward regression and goal ordering techniques. Backward regression helps the algorithm focus on actions that are relevant to the achievement of the goal. Goal ordering techniques strengthens this filtering property, forcing the Forward Search process to consider actions that are relevant at the current stage of the Search process. One of the key features of our planner is its dynamic application of goal ordering techniques: we apply them on the main goal as well as on all the derived sub-goals. We compare the performance of our planner with FF — the winner of the AIPS'00 planning competition — on a number of well-known and novel domains. We show that our planner is competitive with FF, outperforming it on more complex domains in which sub-goals are typically non-trivial.

  • Distributed Heuristic Forward Search for Multi-Agent Systems.
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Raz Nissim, Ronen I. Brafman
    Abstract:

    This paper describes a number of distributed Forward Search algorithms for solving multi-agent planning problems. We introduce a distributed formulation of non-optimal Forward Search, as well as an optimal version, MAD-A*. Our algorithms exploit the structure of multi-agent problems to not only distribute the work efficiently among different agents, but also to remove symmetries and reduce the overall workload. The algorithms ensure that private information is not shared among agents, yet computation is still efficient -- outperforming current state-of-the-art distributed planners, and in some cases even centralized Search -- despite the fact that each agent has access only to partial information.

  • IJCAI - Hierarchical heuristic Forward Search in Stochastic domains
    2007
    Co-Authors: Nicolas Meuleau, Ronen I. Brafman
    Abstract:

    Many MDPs exhibit an hierarchical structure where the agent needs to perform various subtasks that are coupled only by a small sub-set of variables containing, notably, shared resources. Previous work has shown how this hierarchical structure can be exploited by solving several sub-MDPs representing the different subtasks in different calling contexts, and a root MDP responsible for sequencing and synchronizing the subtasks, instead of a huge MDP representing the whole problem. Another important idea used by efficient algorithms for solving flat MDPs, such as (L)AO and (L)RTDP, is to exploit reachability information and an admissible heuristics in order to accelerate the Search by pruning states that cannot be reached from a given starting state under an optimal policy. In this paper, we combine both ideas and develop a variant of the AO* algorithm for performing Forward heuristic Search in hierarchical models. This algorithm shows great performance improvements over hierarchical approaches using standard MDP solvers such as Value Iteration, as well as with respect to AO applied to a flat representation of the problem. Moreover, it presents a general new method for accelerating AO and other Forward Search algorithms. Substantial performance gains may be obtained in these algorithms by partitioning the set of Search nodes, and solving a subset of nodes completely before propagating the results to other subsets.

  • Forward Search value iteration for pomdps
    International Joint Conference on Artificial Intelligence, 2007
    Co-Authors: Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony
    Abstract:

    Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods which quickly converge to an approximate solution formedium-sized problems. Of this family HSVI, which uses trial-based asynchronous value iteration, can handle the largest domains. In this paper we suggest a new algorithm, FSVI, that uses the underlying MDP to traverse the belief space towards rewards, finding sequences of useful backups, and show how it scales up better than HSVI on larger benchmarks.

Anthony C. Atkinson - One of the best experts on this subject based on the ideXlab platform.

  • Robust Bayesian regression with the Forward Search: theory and data analysis
    TEST, 2017
    Co-Authors: Anthony C. Atkinson, Aldo Corbellini, Marco Riani
    Abstract:

    The frequentist Forward Search yields a flexible and informative form of robust regression. The device of fictitious observations provides a natural way to include prior information in the Search. However, this extension is not straightForward, requiring weighted regression. Bayesian versions of Forward plots are used to exhibit the presence of multiple outliers in a data set from banking with 1903 observations and nine explanatory variables which shows, in this case, the clear advantages from including prior information in the Forward Search. Use of observation weights from frequentist robust regression is shown to provide a simple general method for robust Bayesian regression.

  • Cluster detection and clustering with random start Forward Searches
    Journal of Applied Statistics, 2017
    Co-Authors: Anthony C. Atkinson, Marco Riani, Andrea Cerioli
    Abstract:

    The Forward Search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the Forward Search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the Forward Search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with 8 clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.

  • Introducing Prior Information into the Forward Search for Regression
    Topics on Methodological and Applied Statistical Inference, 2016
    Co-Authors: Anthony C. Atkinson, Aldo Corbellini, Marco Riani
    Abstract:

    The Forward Search provides a flexible and informative form of robust regression. We describe the introduction of prior information into the regression model used in the Search through the device of fictitious observations. The extension to the Forward Search is not entirely straightForward, requiring weighted regression. Forward plots are used to exhibit the effect of correct and incorrect prior information on inferences.

  • The Forward Search: Theory and data analysis
    Journal of the Korean Statistical Society, 2010
    Co-Authors: Anthony C. Atkinson, Marco Riani, Andrea Cerioli
    Abstract:

    The Forward Search is a powerful general method, incorporating flexible data-driven trimming, for the detection of outliers and unsuspected structure in data and so for building robust models. Starting from small subsets of data, observations that are close to the fitted model are added to the observations used in parameter estimation. As this subset grows we monitor parameter estimates, test statistics and measures of fit such as residuals. The paper surveys theoretical development in work on the Forward Search over the last decade. The main illustration is a regression example with 330 observations and 9 potential explanatory variables. Mention is also made of procedures for multivariate data, including clustering, time series analysis and fraud detection.

  • Econometric Applications of the Forward Search in Regression: Robustness, Diagnostics, and Graphics
    Econometric Reviews, 2008
    Co-Authors: Anthony C. Atkinson
    Abstract:

    The article illustrates the use of the Forward Search to provide robust analyses of econometric data. The emphasis is on informative plots that reveal the inferential importance of each observation. The division of observations into “good” and “bad” leverage points is shown to be potentially misleading.

Jorg Hoffmann - One of the best experts on this subject based on the ideXlab platform.

  • probabilistic planning via heuristic Forward Search and weighted model counting
    arXiv: Artificial Intelligence, 2011
    Co-Authors: Carmel Domshlak, Jorg Hoffmann
    Abstract:

    We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic Forward-Search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FFs techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the Search space and the heuristic function. Our evaluation of Probabilistic-FF shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further reSearch.

  • Conformant planning via heuristic Forward Search: a new approach
    Artificial Intelligence, 2006
    Co-Authors: Jorg Hoffmann, Ronen I. Brafman
    Abstract:

    Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a Search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that Search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic Forward-Search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.

  • contingent planning via heuristic Forward Search with implicit belief states
    International Conference on Automated Planning and Scheduling, 2005
    Co-Authors: Jorg Hoffmann, Ronen I. Brafman
    Abstract:

    Contingent planning is the task of generating a conditional plan given uncertainty about the initial state and action effects, but with the ability to observe some aspects of the current world state. Contingent planning can be transformed into an And-Or Search problem in belief space, the space whose elements are sets of possible worlds. In (Brafman & Hoffmann 2004), we introduced a method for implicitly representing a belief state using a propositional formula that describes the sequence of actions leading to that state. This representation trades off space for time and was shown to be quite effective for conformant planning within a heuristic Forward-Search planner based on the FF system. In this paper we apply the same architecture to contingent planning. The changes required to adapt the Search space representation are small. More effort is required to adapt the relaxed planning problems whose solution informs the Forward Search algorithm. We propose the targeted use of an additional relaxation, mapping the relaxed contingent problem into a relaxed conformant problem. Experimental results show that the resulting planning system, Contingent-FF, is highly competitive with the state-of-the-art contingent planners POND and MBP.

  • ICAPS - Contingent planning via heuristic Forward Search with implicit belief states
    2005
    Co-Authors: Jorg Hoffmann, Ronen I. Brafman
    Abstract:

    Contingent planning is the task of generating a conditional plan given uncertainty about the initial state and action effects, but with the ability to observe some aspects of the current world state. Contingent planning can be transformed into an And-Or Search problem in belief space, the space whose elements are sets of possible worlds. In (Brafman & Hoffmann 2004), we introduced a method for implicitly representing a belief state using a propositional formula that describes the sequence of actions leading to that state. This representation trades off space for time and was shown to be quite effective for conformant planning within a heuristic Forward-Search planner based on the FF system. In this paper we apply the same architecture to contingent planning. The changes required to adapt the Search space representation are small. More effort is required to adapt the relaxed planning problems whose solution informs the Forward Search algorithm. We propose the targeted use of an additional relaxation, mapping the relaxed contingent problem into a relaxed conformant problem. Experimental results show that the resulting planning system, Contingent-FF, is highly competitive with the state-of-the-art contingent planners POND and MBP.

  • conformant planning via heuristic Forward Search a new approach
    International Conference on Automated Planning and Scheduling, 2004
    Co-Authors: Ronen I. Brafman, Jorg Hoffmann
    Abstract:

    Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a Search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that Search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by deciding solvability of CNFs that capture the action sequence's semantics. Based on this approach, we extend the classical heuristic planning system FF to the conformant setting. The key to this extension is the introduction of approximative CNF reasoning in FF's heuristic function. Our experimental evaluation shows Conformant-FF to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.

Chitradipa Chakraborty - One of the best experts on this subject based on the ideXlab platform.