Proplant: Multiagent system for production planning

The so-called ''tribase'' acquaintance model of the agent's behavior is presented in this paper. This represents an extension of the twin-base model (Cao et al., 1997). Based on practical experience, the new model tries to cope with parallel processing, precedence constraints, and sparse resources. The idea of substituting the interagent negotiation processes by the periodical internal planning activity of the agents is stressed. A multiagent system, ProPlanT, as an application of the tribase model for the project-oriented production planning developed for TESLA TV company is described in detail. Three types of agents production planning agent (PPA), production management agent (PMA), and production agent (PA) are distinguished. The corresponding tribase models and potential role of metaagents are discussed.     

Multi-Frame Correspondence Estimation Using Subspace Constraints

When a rigid scene is imaged by a moving camera, the set of all displacements of all points across multiple frames often resides in a low-dimensional linear subspace. Linear subspace constraints have been used successfully in the past for recovering 3D structure and 3D motion information from multiple frames (e.g., by using the factorization method of Tomasi and Kanade (1992, International Journal of Computer Vision, 9:137–154)). These methods assume that the 2D correspondences have been precomputed. However, correspondence estimation is a fundamental problem in motion analysis. In this paper we show how the multi-frame subspace constraints can be used for constraining the 2D correspondence estimation process itself.We show that the multi-frame subspace constraints are valid not only for affine cameras, but also for a variety of imaging models, scene models, and motion models. The multi-frame subspace constraints are first translated from constraints on correspondences to constraints directly on image measurements (e.g., image brightness quantities). These brightness-based subspace constraints are then used for estimating the correspondences, by requiring that all corresponding points across all video frames reside in the appropriate low-dimensional linear subspace.The multi-frame subspace constraints are geometrically meaningful, and are {not} violated at depth discontinuities, nor when the camera-motion changes abruptly. These constraints can therefore replace {heuristic} constraints commonly used in optical-flow estimation, such as spatial or temporal smoothness.     

The Power of Commuting with Finite Sets of Words

We construct a finite language L such that the largest language commuting with L is not recursively enumerable. This gives a negative answer to the question raised by Conway in 1971 and also strongly disproves Conway's conjecture on context-freeness of maximal solutions of systems of semi-linear inequalities.     

The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

When a company decides to automate its business processes by means of RPA (Robotic Process Automation), there are two fundamental questions that need to be answered. Firstly, what activities should the company automate and what characteristics make them suitable for RPA. The aim of the presented research is to design and demonstrate a data-driven performance framework assessing the impact of RPA implementation using process mining (PPAFR). Firstly, we comment on and summarise existing trends in process mining and RPA. Secondly, we describe research objectives and methods following the Design Science Research Methodology. Then, we identify critical factors for RPA implementation and design process stages of PPAFR. We demonstrate the design on real data from a loan application process. The demonstration consists of a process discovery using process mining methods, process analysis, and process simulation with assessment of RPA candidates. Based on the research results, a redesign of the process is proposed with emphasis on RPA implementation. Finally, we discuss the usefulness of PPAFR by helping companies to identify potentially suitable activities for RPA implementation and not overestimating potential gains. Obtained results show that within the loan application process, waiting times are the main causes of extended cases. If the waiting times are generated internally, it will be much easier for the company to address them. If the automation is focused mainly on processing times, the impact of automation on the overall performance of the process is insignificant or very low. Moreover, the research identified several characteristics which have to be considered when implementing RPA due to the impact on the overall performance of the process.

     

Incremental Branching Programs

We propose a new model of restricted branching programs specific to solving GEN problems, which we call incremental branching programs. We show that syntactic incremental branching programs capture previously studied models of computation for the problem GEN, namely marking machines (Cook, S.A. in J. Comput. Syst. Sci. 9(3):308–316, 1974) and Poon’s extension (Proc. of the 34th IEEE Symp. on the Foundations of Computer Science, pp. 218–227, 1993) of jumping automata on graphs (Cook, S.A., Rackoff, C.W. in SIAM J. Comput. 9:636–652, 1980). We then prove exponential size lower bounds for our syntactic incremental model, and for some other variants of branching program computation for GEN. We further show that nondeterministic syntactic incremental branching programs are provably stronger than their deterministic counterpart when solving a natural NL-complete GEN sub-problem. It remains open if syntactic incremental branching programs are as powerful as unrestricted branching programs for GEN problems. A preliminary version of this paper appears as (Gál, A., Koucky, M., McKenzie, P., Incremental branching programs, in Proc. of the 2006 Computer Science in Russia Conference CSR06. Lecture Notes in Computer Science, vol. 3967, pp. 178–190, 2006). A. Gal supported in part by NSF Grant CCF-0430695 and an Alfred P. Sloan Research Fellowship. M. Koucky did part of this work while being a postdoctoral fellow at McGill University, Canada and at CWI, Amsterdam, Netherlands. Supported in part by NWO vici project 2004–2009, project No. 1M0021620808 of MŠMT ČR, grants 201/07/P276, 201/05/0124 of GA ČR, and Institutional Research Plan No. AV0Z10190503. P. McKenzie supported by the NSERC of Canada and the (Québec) FQRNT.     

Characterization of Combinatorially Independent Permutation Separability Criteria

The so-called permutation separability criteria are simple operational conditions that are necessary for separability of mixed states of multipartite systems: (1) permute the indices of the density matrix and (2) check if the trace norm of at least one of the resulting operators is greater than one. If it is greater than one then the state is necessarily entangled. A shortcoming of the permutation separability criteria is that many permutations give rise to equivalent separability criteria. Therefore, we introduce a necessary condition for two permutations to yield independent criteria called combinatorial independence. This condition basically means that the map corresponding to one permutation cannot be obtained by concatenating the map corresponding to the second permutation with a norm-preserving map. We characterize completely combina-torially independent criteria, and determine simple permutations that represent all independent criteria. The representatives can be visualized by means of a simple graphical notation. They are composed of three basic operations: partial transpose, and two types of so-called reshufflings. In particular, for a four-partite system all criteria except one are composed of partial transpose and only one type of reshuffling; the exceptional one requires the second type of reshuffling. Furthermore, we show how to obtain efficiently a simple representative for every permutation. This method allows to check easily if two permutations are Combinatorially equivalent or not.     

Method of classifier selection using the genetic approach

Abstract: The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.     

Supervised relevance maps for increasing the distinctiveness of facial images

This paper shows how to improve holistic face analysis by assigning importance factors to different facial regions (termed as face relevance maps). We propose a novel supervised learning algorithm for generating face relevance maps to improve the discriminating capability of existing methods. We have successfully applied the developed technique to face identification based on the Eigenfaces and Fisherfaces methods, and also to gender classification based on principal geodesic analysis (PGA). We demonstrate how to iteratively learn the face relevance map using labelled data. Experimental results confirm the effectiveness of the developed approach.