Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.Comment: See http://www.jair.org/ for any accompanying file

European welfare state under the policy "make work pay" : Analysis with composite indicators

The social security systems in 22 European countries are evaluated with a specially constructed indicator. It is based on a census-simulating model which combines both empirical (statistical) and normative (rule-based) approaches. The individual answers of unemployed on social security benefits are normatively derived from their personal situations with the OECD Tax-Benefit Models. The empirical data about personal situations are available from EuroStat. The goal is estimating the national average of net replacement rates (NRR) for unemployed persons. Such an indicator of social security shows the average degree with which social benefits compensate the loss of previous earnings. Thus, the paper suggests: -(Methodology) a model of census simulation combining statistical data on the population with individual answers computed with a rule-based model, -(Indicator) an integral quantitative evaluation of social security in Europe, which reveals its total decline by 2004 contrary to institutional improvements, -(Analysis) an explanation of the decline by a structural change of European labour markets with rapidly growing `atypical' employment groups (= part-time, temporary, self-employed, etc.) with a lower eligibility to social benefits than normally employed (= permanently full-time), -(Policy implications) a possible resolution of European policy contradictions by the "basic income model" with "flexinsurance". --Composite indicators,social security,European welfare state,European Union,"make work pay" policy

Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products

Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy