Neurogenetic Programming Framework for Explainable Reinforcement Learning

Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer: using evolutionary methods as an alternative to gradient descent for neural network training}, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.Comment: Source code is available at https://github.com/vadim0x60/cib

BF++: a language for general-purpose program synthesis

Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge into the models or let experts review and validate the learned decision mechanisms. Knowledge-insertion and model review are important requirements in many applications involving human health and safety. One way to bridge the gap between data and knowledge driven systems is program synthesis: replacing a neural network that outputs decisions with a symbolic program generated by a neural network or by means of genetic programming. We propose a new programming language, BF++, designed specifically for automatic programming of agents in a Partially Observable Markov Decision Process (POMDP) setting and apply neural program synthesis to solve standard OpenAI Gym benchmarks.Comment: 8+2 pages (paper+references

Schema-Driven Actionable Insight Generation and Smart Recommendation

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback

Comparative study of seed yield and seed quality of advanced lines and commercial varieties of red clover (Trifolium pratense L.)

Red clover (Trifolium pratense L.) is one of the most important forage legumes in areas with acidic and nutrient poorer soils where alfalfa fails to growth. In 2010-2011 years period we studied four advanced lines and four commercial varieties of red clover, which are widely used in the production in Bosnia and Herzegovina. Our results showed that the variation in grain yield, thousand kernel weight and germination energy was under control of growth. Across genotypes seed yield in 2010 and 2011 was 205 and 223 kg ha(-1), respectively. The highest yield of seed was obtained from second growth in the second year. Extreme precipitation during anthesis and grain filling and ripening in 2010 negatively affected red clover seed production. Advanced line DS-2 had the highest grain yield (234 kg ha(-1)) and thousand kernel weight (1.75 g). Low seeds yields of the tested genotypes are questioning the cost-effectiveness of red clover seed production at this site

Neural Scoring of Logical Inferences from Data using Feedback

Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data

MAXIMIZING SALES UNDER CONDITIONS OF NONLENARTY

This paper deals with the problem of maximizing the sales of a particular product when the revenue function is nonlinear in dependence of the demand for that product. This type of problem is usually solved by the nonlinear programming method which has been sufficiently described in mathematical theory; however, its use is not that simple. Solving functions of more than two variables is rather complicated and requires an appropriate mathematical model as well as suitable software for computer solving of the given problem, which sometimes involves team work.Key words: Nonlinear programming, Kuhn-Tucker conditions, revenue function, deman

Schema-Driven Actionable Insight Generation and Smart Recommendation

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback

Schema-Driven Actionable Insight Generation and Smart Recommendation

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback

PRAVNI POLOŽAJ I ULOGA BANKE RUSIJE U OBEZBEĐENJU EKONOMSKOG SUVERENITETA RUSKE FEDERACIJE

The Bank of Russia is the legal entity which, apart of the other organs of state authorities, acts as the organ of state governing with remarkable role in exibiting the functions of Russian state and insufficiently precisely determined status in accordance to the state authorities and law entities. In this work the investigation is directed to the relationship between the legal position of the Bank of Russia and its role in providing the economic sovereignty of the Russian Federation. The aim of the work is to explore the legal position of main bank in monetary system and its role in providing the economic sovereignty of the Russian Federation on the basis of systematization and reliable literature source norm analysis, applying the comparative-law method, as well as the method of legal exegesis and content analysis. The results of survey imply to the presence of different attitudes to the legal position of the Bank of Russia. Insufficiently determined legal position of the Bank of Russia brings to disballance of measures and activities of executive authorities and the Bank of Russia, which has a negative influence on providing the full economic sovereignty, self-developement of the country and greater social benefits, in spite of the fact that the Bank of Russia is mostly independent from the state authorities having a comfortable position in civil transit affairs.Banka Rusije je pravni entitet koji, nezavisno od drugih organa državne vlasti, postupa kao organ državnog upravljanja sa značajnom ulogom u sprovođenju funkcija ruske države i nedovoljno precizno određenim statusom u odnosu na organe državne vlasti i pravna lica. U radu je istraživanje usmereno na odnos između pravnog položaja Banke Rusije i njene uloge u postizanju ekonomskog suvereniteta Ruske Federacije (RF).   Cilj rada je da se, na bazi sistematizacije i analize relevantnih izvora iz literature i normativne regulative, primenom uporednopravne metode, metode pravne egzegeze i analize sadržaja, ispita pravni položaj glavne banke u monetarnom sistemu i njena uloga u ostvarivanju ekonomskog suvereniteta RF. Rezultati ispitivanja ukazuju na postojanje različitih mišljenja o pravnom položaju Banke Rusije.   Nedovoljno precizno određen položaj Banke Rusije dovodi do neusklađenosti mera i aktivnosti izvršne vlasti i Banke Rusije, što se negativno odražava na postizanje ekonomskog suvereniteta, samostalni razvoj zemlje i povećanje društvenog blagostanja, uprkos tome što je Banka Rusije u velikoj meri nezavisna od organa državne vlasti i ima komfornu poziciju u pravnom prometu

ADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR PRECIPITATION ESTIMATION

The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN) can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. Due to its being a crucial problem, this paper presents a process constructed so as to simulate precipitation with an adaptive neuro-fuzzy inference (ANFIS) method. ANFIS is a specific type of the ANN family and shows very good learning and prediction capabilities, which makes it an efficient tool for dealing with encountered uncertainties in any system such as precipitation. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated.  The simulation results presented in this paper show the effectiveness of the developed method