Limited discrepancy search revisited

Prosser, Patrick; Unsworth, Chris

doi:10.1145/1963190.2019581

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…Hence, NAXOS supports a plethora of search methods such as Depth First Search, Limited Discrepancy Search [26], etc. In this work, we mainly use a Depth-bounded Backtrack Search (DBS) method [27].…”

Section: B Search Methods and 'Naxos' Solvermentioning

confidence: 99%

Course Scheduling in an Adjustable Constraint Propagation Schema

Pothitos

Stamatopoulos

Zervoudakis

2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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Abstract-Constraint Programming constitutes a prominent paradigm for solving time-consuming Constraint Satisfaction Problems (CSPs). In this work, at first we model a generic course scheduling problem as a CSP, that complies with the International Timetabling Competition (ITC) standards. Constraint Programming allowed us to search for a solution via several state-of-the-art methodologies and compare them. For the stochastic search methods, we propose new hybrid semi-random heuristics. Second, we chose to maintain bounds consistency during search to prune 'no-good' branches of the search tree. We theoretically define new lightweight consistency types, namely k-bounds-consistency, in order to speed up the overall search procedure. Eventually, we process real world data and show the efficiency of our proposal: While plain backtracking produces poor results, constraint propagation dramatically boosts the solutions quality, and can be 'finetuned' in our adjustable schema to make it even faster.

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Section: B Search Methods and 'Naxos' Solvermentioning

confidence: 99%

Course Scheduling in an Adjustable Constraint Propagation Schema

Pothitos

Stamatopoulos

Zervoudakis

2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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“…We argue that it should be possible to prioritise plan choice based on plan characteristics, but not assume a totally fixed ordering in order to allow exploration of non-highest order plans that might have better properties. This is akin to discrepancy search techniques [29] to go against the heuristic, and is particularly useful for declarative goals to avoid repeating the same plan obsessively.…”

Section: Plan Selection Strategiesmentioning

confidence: 99%

Probabilistic BDI Agents: Actions, Plans, and Intentions

Archibald

Calder

Sevegnani

et al. 2021

Lecture Notes in Computer Science

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The Belief-Desire-Intention (BDI) architecture is a popular framework for rational agents, yet most verification approaches are limited to analysing qualitative properties, for example whether an intention completes. BDI-based systems, however, operate in uncertain environments with dynamic behaviours: we may need quantitative analysis to establish properties such as the probability of eventually completing an intention. We define a probabilistic extension to the Conceptual Agent Notation (CAN) for BDI agents that supports probabilistic action outcomes, and probabilistic plan and intention selection. The semantics is executable via an encoding in Milner's bigraphs and the BigraphER tool. Quantitative analysis is conducted using PRISM. While the new semantics can be applied to any CAN program, we demonstrate the extension by comparing with standard plan and intention selection strategies (e.g. ordered or fixed schedules) and evaluating probabilistic action executions in a smart manufacturing scenario. The results show we can improve significantly the probability of intention completion, with appropriate probabilistic distribution. We also show the impact of probabilistic action outcomes can be marginal, even when the failure probabilities are large, due to the agent making smarter intention selection choices.

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“…For this reason, we propose a simple method that helps heuristics to make better decisions at the very first stages of the search. Our method differs from the well-known LDS [ 45 – 47 ] in the fact that we do not refute the decisions of the heuristics, but we use such heuristics on reduced instances in order to estimate which variable, once instantiated, is likely to reduce the cost of the search.…”

Section: Heuristics-related Experimentsmentioning

confidence: 99%

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

Ortíz-Bayliss

Amaya

Conant-Pablos

et al. 2018

Computational Intelligence and Neuroscience

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When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

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Limited discrepancy search revisited

Cited by 14 publications

References 9 publications

Course Scheduling in an Adjustable Constraint Propagation Schema

Course Scheduling in an Adjustable Constraint Propagation Schema

Probabilistic BDI Agents: Actions, Plans, and Intentions

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

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