Colin P. Williams

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8ranked-venue papers

4as first author

0since 2021 · last 1996

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 8 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 4 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 5 papers		Learning theory · 55% Planning, search and constraint satisfaction · 24% Multi-agent systems · 21%
Theoretical computer science 3 papers		Computational complexity · 65% Algorithms and data structures · 35%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Parallel and multicore computing · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Learning theory phase transition	2	1996	Phase Transitions and the Search Problem · Artif. Intell. 1996 The Hardest Constraint Problems: A Double Phase Transition · Artif. Intell. 1994
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › constraint programming parallel constraint solving	1	1994	Expected Gains from Parallelizing Constraint Solving for Hard Problems · AAAI 1994
Knowledge, reasoning and agents › Multi-agent systems › multi-robot coordination cooperative search	1	1993	Solving the Really Hard Problems with Cooperative Search · AAAI 1993
Algorithms and data structures search algorithms	1	1993	Solving the Really Hard Problems with Cooperative Search · AAAI 1993
Parallel and multicore computing parallel algorithms	1	1994	Expected Gains from Parallelizing Constraint Solving for Hard Problems · AAAI 1994

Methods — techniques the papers use, named apart from their topics

cooperative search · 0.0constraint satisfaction · 0.0

Year	Publication	Venue	Position
1996	Phase Transitions and the Search Problem Tad Hogg, Bernardo A. Huberman, Colin P. Williams	Artif. Intell.	3
1994	Expected Gains from Parallelizing Constraint Solving for Hard Problems Tad Hogg, Colin P. Williams	AAAI	2
1994	The Hardest Constraint Problems: A Double Phase Transition Tad Hogg, Colin P. Williams	Artif. Intell.	2
1994	Exploiting the Deep Structure of Constraint Problems Colin P. Williams, Tad Hogg	Artif. Intell.	1
1993	Solving the Really Hard Problems with Cooperative Search Tad Hogg, Colin P. Williams	AAAI	2
1993	Extending Deep Structure Colin P. Williams, Tad Hogg	AAAI	1
1993	The Typicality of Phase Transitions in Search abstract Search is fundamental to artificial intelligence (AI) and numerous sophisticated search methods have been developed. We present a general, simple model of search processes and use it to analytically determine some typical behavior when applied to large problems. In particular, this identifies abrupt changes in overall search cost as small improvements are made in the underlying method. We also examine the robustness of this model's predictions in a range of more realistic cases. More generally, we introduce a criterion for determining when average case results reflect typical behavior which allows the method developed here to be used for investigating other large‐scale behaviors of complex AI systems. Colin P. Williams, Tad Hogg	Comput. Intell.	1
1992	Using Deep Structure to Locate Hard Problems Colin P. Williams, Tad Hogg	AAAI	1