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

Views on Vision

MIT Artificial Intelligence Laboratory Vision Grou

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

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

Active Knowledge

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005. Vision Flashes are informal papers intended for internal use.A progress report on the work described in Vision Flashes 33 and 43 on recognition of real objects. Emphasis is on the "active" use of knowledge in directing the flow of visual processing.MIT Artificial Intelligence Laboratory Robotics Section Department of Defense Advanced Research Projects Agenc

Relating the structure of a problem and its explanation

As AI becomes more ubiquitous there is increasing interest in computers being able to provide explanations for their conclusions. This paper proposes exploring the relationship between the structure of a problem and its explanation. The nature of this challenge is introduced through a series of simple constraint satisfaction problems

The Object Partition Problem

MIT Artificial Intelligence Laboratory Vision Grou

Recognition of Real Objects

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0003. Vision flashes are informal papers intended for internal use.High level semantic knowledge will be employed in the development of a machine vision program flexible enough to deal with a class of "everyday objects" in varied environments. This report is in the nature of a thesis proposal for future work.MIT Artificial Intelligence Laboratory Robotics Section Department of Defense Advanced Research Projects Agenc

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