Progress towards the Holy Grail

Freuder, Eugene C.

doi:10.1007/s10601-017-9275-0

Cited by 25 publications

(18 citation statements)

References 57 publications

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“…If the answer of the user is positive, the set κ B (e Y ) of all the constraints from B that reject e Y is removed from the bias (line 9). In case the answer is negative, it tries to learn a constraint by using the functions FindScope-2 and FindC (lines [14][15][16][17].…”

Section: Ignoring Omissionsmentioning

confidence: 99%

“…Expressing a combinatorial problem as a constraint network requires considerable expertise in the field. Hence, one of the major challenges in CP research is that of efficiently obtaining a good model of a real problem without relying on expert human modellers [14,16,15]. Constraint acquisition can assist non-expert users in modeling a constraint problem automatically.…”

Section: Introductionmentioning

confidence: 99%

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Omissions in Constraint Acquisition

Tsouros

Stergiou

Bessière

2020

Lecture Notes in Computer Science

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Interactive constraint acquisition is a special case of query-directed learning, also known as "exact" learning. It is used to assist non-expert users in modeling a constraint problem automatically by posting examples to the user that have to be classified as solutions or non-solutions. One significant issue that has not been addressed in the literature of constraint acquisition is the possible presence of uncertainty in the answers of the users. We address this by introducing Limited Membership Queries, where the user has the option of replying "I don't know", corresponding to "omissions" in exact learning. We present two algorithms for handling omissions. The first one deals with omissions that are independent events, while the second assumes that omissions are related to gaps in the user's knowledge. We present theoretical results about both methods and we evaluate them on benchmark problems. Importantly, our second algorithm can not only learn (a part of) the target network, but also the constraints that cause the user's uncertainty.

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Section: Ignoring Omissionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Omissions in Constraint Acquisition

Tsouros

Stergiou

Bessière

2020

Lecture Notes in Computer Science

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“…Our main thesis is to still formulate computations as the sum product problem, but we will rigorously define a semantics that not only defines AMC in unbounded domains, but also non-standard (e.g., non-monotonic) ones. To put the proposal in perspective, consider that Eugene Freuder [37] famously quipped: "constraint programming represents one of the closest approaches computer science has yet made to the Holy Grail of programming: the user states the problem, the computer solves it." The underlying idea was summarized in the slogan:…”

Section: Introductionmentioning

confidence: 99%

Semiring programming: A semantic framework for generalized sum product problems

Belle

Raedt

2020

International Journal of Approximate Reasoning

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To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving real-world problems requires an integration amongst these, contemporary representation methodologies offer little support for this.In an attempt to alleviate this situation, we position and motivate a new declarative programming framework in this paper. We focus on the semantical foundations in service of providing abstractions of well-known problems such as SAT, Bayesian inference, generative models, learning and convex optimization. Programs are understood in terms of first-order logic structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines and represent non-standard problems over unbounded domains. Thus, the main thrust of this paper is to view such well-known problems through a unified lens in the hope that appropriate solver strategies (exact, approximate, portfolio or hybrid) may emerge that tackle real-world problems in a principled way.

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“…Designed to solve optimisation and decision problems, it provides expressive modelling languages, development tools and global constraints. An overview of the current status of CP and its challenges can be found in [9].…”

Section: Introductionmentioning

confidence: 99%

Modelling Dynamic Programming-Based Global Constraints in Constraint Programming

Visentin

Prestwich

Rossi

et al. 2019

Advances in Intelligent Systems and Computing

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Dynamic Programming (DP) can solve many complex problems in polynomial or pseudo-polynomial time, and it is widely used in Constraint Programming (CP) to implement powerful global constraints. Implementing such constraints is a nontrivial task beyond the capability of most CP users, who must rely on their CP solver to provide an appropriate global constraint library. This also limits the usefulness of generic CP languages, some or all of whose solvers might not provide the required constraints. A technique was recently introduced for directly modelling DP in CP, which provides a way around this problem. However, no comparison of the technique with other approaches was made, and it was missing a clear formalisation. In this paper we formalise the approach and compare it with existing techniques on MiniZinc benchmark problems, including the flow formulation of DP in Integer Programming. We further show how it can be improved by state reduction methods.

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Progress towards the Holy Grail

Cited by 25 publications

References 57 publications

Omissions in Constraint Acquisition

Omissions in Constraint Acquisition

Semiring programming: A semantic framework for generalized sum product problems

Modelling Dynamic Programming-Based Global Constraints in Constraint Programming

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