Explaining ourselves: human-aware constraint reasoning

Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundation for human-awareness should enable ourselves and our “AIs” to “explain ourselves” naturally to each other. Constraint reasoning offers particular opportunities and challenges in this regard. This paper takes note of the history of work in this area and encourages increased attention, laying out a rough research agenda

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Generalizing inconsistency learning for constraint satisfaction

Constraint satisfaction problems, where values are sought for problem variables subject to restrictions on which combinations of values are acceptable, have many applications in artificial intelligence. Conventional learning methods acquire individual tuples of inconsistent values. These learning experiences can be generalized. We propose a model of generalized learning, based on inconsistency preserving mappings, which is sufficiently focused so as to be computationally cost effective. Rather than recording an individual inconsistency that led to a failure, and looking for that specific inconsistency to recur, we observe the context of a failure, and then look for a related context in which to apply our experience opportunistically. As a result we leverage our learning power. This model is implemented, extended and evaluated using two simple but important classes of constraint problems

Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems

Model debugging is an important component of assisting modelers with constraint-based problem formulation. This paper is built around a case study in modeling a special class of CSPs, which represent problems that change when certain conditions are met (Mittal & Falkenhainer 1990). The control of changing the problem, by activating or deactivating variables, is part of the problem representation and is modeled through special constraints, called activity constraints. The activity constraints may interact with the other constraints and generate inconsistencies or redundancies. We present initial examples of these two types of iteractions, and we derive more general forms of inconsistency and redundancy. We believe this work can lead to methods for automatic model debugging, which detect and resolve problems with existing models

Interleaving solving and elicitation of constraint satisfaction problems based on expected cost

We consider Constraint Satisfaction Problems in which constraints can be initially incomplete, where it is unknown whether certain tuples satisfy the constraint or not. We assume that we can determine the satisfaction of such an unknown tuple, i.e., find out whether this tuple is in the constraint or not, but doing so incurs a known cost, which may vary between tuples. We also assume that we know the probability of an unknown tuple satisfying a constraint. We define algorithms for this problem, based on backtracking search. Specifically, we consider a simple iterative algorithm based on a cost limit on the unknowns that may be determined, and a more complex algorithm that delays determining an unknown in order to estimate better whether doing so is worthwhile. We show experimentally that the more sophisticated algorithms can greatly reduce the average cost

Soft constraint abstraction based on semiring homomorphism

The semiring-based constraint satisfaction problems (semiring CSPs), proposed by Bistarelli, Montanari and Rossi \cite{BMR97}, is a very general framework of soft constraints. In this paper we propose an abstraction scheme for soft constraints that uses semiring homomorphism. To find optimal solutions of the concrete problem, the idea is, first working in the abstract problem and finding its optimal solutions, then using them to solve the concrete problem. In particular, we show that a mapping preserves optimal solutions if and only if it is an order-reflecting semiring homomorphism. Moreover, for a semiring homomorphism $\alpha$ and a problem $P$ over $S$, if $t$ is optimal in $\alpha(P)$, then there is an optimal solution $\bar{t}$ of $P$ such that $\bar{t}$ has the same value as $t$ in $\alpha(P)$.Comment: 18 pages, 1 figur

Neighborhood Interchangeability and Dynamic Bundling for Non-binary CSPs

1. Interchangeability: An algorithm for computing interchangeability in non-binary CSPs. 2. Dynamic bundling: Integration of the above with backtrack search for solving non-binary CSPs. 3. Experiments demonstrating the benefits of dynamic bundling ·Finding multiple, robust solutions. ·Decreasing computational cost of search

A constraint-based approach to fault management for groupware services, Integrated Network Management, 1999

There is no standard model at the service layer. However, fault management to distributed services and applications needs to construct and utilize complex models of the participating objects and their interdependencies. Thus, model-based fault management tools can predict the correct behavior of diagnosed systems and use the resulting predictions to identify faults. When used on-line in real systems, diagnostic tools based on such models should be able to provide prompt response and accurate, comprehensive explanations of the root causes of faults. In this paper we propose to address these requirements: modeling, proactive diagnosis, and explanation. We apply a recent extension to the constraint satisfaction paradigm, called composite constraint satisfaction, to facilitate modeling of complex systems, and we use constraint propagation techniques to support proactive diagnosis and explanation. We demonstrate the applicability of our approach on an example of a basic groupware service, namely, distributed database replication

Neighborhood Interchangeability and Dynamic Bundling for Non-binary CSPs

1. Interchangeability: An algorithm for computing interchangeability in non-binary CSPs. 2. Dynamic bundling: Integration of the above with backtrack search for solving non-binary CSPs. 3. Experiments demonstrating the benefits of dynamic bundling ·Finding multiple, robust solutions. ·Decreasing computational cost of search