Conditional constraint satisfaction and configuration: A win-win proposition

Over the years, a whole sector of AI dealing with configuration problems has emerged, and since 1996, an annual configuration workshop has been held in affiliation with a major AI conference. This installment of Trends & Controversies presents essays from the configuration workshop held in August 2006 as part of ECAI in Riva del Garda, Italy

Optimising ITS behaviour with Bayesian networks and decision theory

We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action. Because normative theories are a general framework for rational behaviour, they can be used to both define and apply learning theories in a rational, and therefore optimal, way. This contrasts to the more traditional approach of using an ad-hoc scheme to implement the learning theory. A key step of the methodology is the induction and the continual adaptation of the Bayesian network student model from student performance data, a step that is distinct from other recent Bayesian net approaches in which the network structure and probabilities are either chosen beforehand by an expert, or by efficiency considerations. The methodology is demonstrated by a description and evaluation of CAPIT, a normative constraint-based tutor for English capitalisation and punctuation. Our evaluation results show that a class using the full normative version of CAPIT learned the domain rules at a faster rate than the class that used a non-normative version of the same system

Relating Weight Constraint and Aggregate Programs: Semantics and Representation

Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that for a broad class of weight constraint programs, called strongly satisfiable programs, the two semantics coincide. When they disagree, a stable model admitted by the stable model semantics may be circularly justified. We show that the gap between the two semantics can be closed by transforming a weight constraint program to a strongly satisfiable one, so that no circular models may be generated under the current implementation of the stable model semantics. We further demonstrate the close relationship between the two semantics by formulating a transformation from weight constraint programs to logic programs with nested expressions which preserves the answer set semantics. Our study on the semantics leads to an investigation of a methodological issue, namely the possibility of compact representation of aggregate programs by weight constraint programs. We show that almost all standard aggregates can be encoded by weight constraints compactly. This makes it possible to compute the answer sets of aggregate programs using the ASP solvers for weight constraint programs. This approach is compared experimentally with the ones where aggregates are handled more explicitly, which show that the weight constraint encoding of aggregates enables a competitive approach to answer set computation for aggregate programs.Comment: To appear in Theory and Practice of Logic Programming (TPLP), 2011. 30 page

Focus of attention in an activity-based scheduler

Earlier research in job shop scheduling has demonstrated the advantages of opportunistically combining order-based and resource-based scheduling techniques. An even more flexible approach is investigated where each activity is considered a decision point by itself. Heuristics to opportunistically select the next decision point on which to focus attention (i.e., variable ordering heuristics) and the next decision to be tried at this point (i.e., value ordering heuristics) are described that probabilistically account for both activity precedence and resource requirement interactions. Preliminary experimental results indicate that the variable ordering heuristic greatly increases search efficiency. While least constraining value ordering heuristics have been advocated in the literature, the experimental results suggest that other value ordering heuristics combined with our variable-ordering heuristic can produce much better schedules without significantly increasing search

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

The Welfare State, Redistribution and the Economy, Reciprocal Altruism, Consumer Rivalry and Second Best

Democratic countries with substantial inequality and where people believe that success depends on connections and luck induce political support for high tax rates and generous welfare states. Traditional wisdom is that such policies harm the economy, but there is not much evidence that countries with a large welfare state and substantial redistribution have worse economic performance and welfare. One important reason is that governments have been careful to invoke the principles of reciprocity and mutual obligations in the design of the welfare state. Unemployment benefits conditioned on work experience, no misconduct and search effort harm the economy less. Indeed, conditional benefits may even boost employment in an economy with efficiency wages. A second reason is that people care about relative incomes and become unhappy if others earn and consume much more than they do. This explains why people do not seem to get happier, even though societies grow richer and richer. With such consumer rivalry the government wishes to correct for the rat race, even if there is no need for redistribution, by taxing labour. A third reason is that in modern economies many distortions are present and removing one at a time may worsen economic performance. Conversely, increasing tax progression in economies with non-competitive labour markets induces wage moderation and boosts employment. A final reason is that countries with large welfare states typically introduce various progrowth policies as well.mutual obligations, altruism, relative incomes, happiness, redistributive taxation, demand management, second best, design of welfare state

Optimal Testing for Planted Satisfiability Problems

We study the problem of detecting planted solutions in a random satisfiability formula. Adopting the formalism of hypothesis testing in statistical analysis, we describe the minimax optimal rates of detection. Our analysis relies on the study of the number of satisfying assignments, for which we prove new results. We also address algorithmic issues, and give a computationally efficient test with optimal statistical performance. This result is compared to an average-case hypothesis on the hardness of refuting satisfiability of random formulas

New welfare policy instruments: international experiences and implications for social enterprises

In this paper recent approaches to the role of social protection systems within economic development policies are discussed. Important experiences are considered, in particular those implemented in medium and low income countries, where new tools for increasing the effectiveness of social and development policies have been tried and tested. Some lessons are also examined that prove useful for defining mechanisms that enhance the quality of services provided by social enterprises in the Italian context.social protection system, development policy, social enterprise