Person: Jörg Denzinger

DBLP: Denzinger:J=ouml=rg

Contributed to:

2013

2009

2009

2009

2008

2008

2007

1996

1995

1993

1989

2005

2007

2009

2011

2011

2012

2012

2013

2015

2019

Wrote 23 papers:

MLDM-2013-GaoD #data mining #distributed #mining #performance #using

Improving the Efficiency of Distributed Data Mining Using an Adjustment Work Flow (JG, JD), pp. 69–83.

ICSM-2009-HolmesCWD #case study #source code #using

The end-to-end use of source code examples: An exploratory study (RH, RC, RJW, JD), pp. 555–558.

SAC-2009-KasingerDB #coordination #distributed

Decentralized coordination of homogeneous and heterogeneous agents by digital infochemicals (HK, JD, BB), pp. 1223–1224.

VISSOFT-2009-CottrellGHWD #source code #visual notation

Compare and contrast: Visual exploration of source code examples (RC, BG, RH, RJW, JD), pp. 29–32.

FSE-2008-CottrellWD #reuse #source code

Semi-automating small-scale source code reuse via structural correspondence (RC, RJW, JD), pp. 214–225.

SAC-2008-SzaboAAD

The tale of the weather worm (JS, JA, RA, JD), pp. 2097–2102.

ESEC-FSE-2007-CottrellCWD

Determining detailed structural correspondence for generalization tasks (RC, JJCC, RJW, JD), pp. 165–174.

CADE-1996-DenzingerS #learning #proving #theorem proving

Learning Domain Knowledge to Improve Theorem Proving (JD, SS), pp. 62–76.

RTA-1995-AvenhausDF #deduction #distributed #equation #named

DISCOUNT: A SYstem for Distributed Equational Deduction (JA, JD, MF), pp. 397–402.

RTA-1993-AvenhausD #equation #proving #theorem proving

Distributing Equational Theorem Proving (JA, JD), pp. 62–76.

RTA-1989-AvenhausDM #named #performance #proving #theorem proving

THEOPOGLES — An efficient Theorem Prover based on Rewrite-Techniques (JA, JD, JM), pp. 538–541.

CIG-2005-DenzingerLGB #behaviour #game studies #testing

Dealing with Parameterized Actions in Behavior Testing of Commercial Computer Games (JD, KL, DG, JWB).

CIG-2005-DenzingerW #behaviour #learning

Combining Coaching and Learning to Create Cooperative Character Behavior (JD, CW).

CIG-2007-FinkDA #behaviour #game studies #machine learning #using

Extracting NPC behavior from computer games using computer vision and machine learning techniques (AF, JD, JA), pp. 24–31.

CIG-2009-MalikD #behaviour #coordination #metaprogramming #multi #testing #using

Improving testing of multi-unit computer players for unwanted behavior using coordination macros (AM, JD), pp. 355–362.

AIIDE-2011-BlackadarD #behaviour #contest #testing

Behavior Learning-Based Testing of Starcraft Competition Entries (MB, JD).

CIG-2011-NygrenDSA #automation #framework #game studies #generative #platform

User-preference-based automated level generation for platform games (NN, JD, BS, JA), pp. 55–62.

AIIDE-2012-Williams-KingDAS #automation #game studies #generative #standard

The Gold Standard: Automatically Generating Puzzle Game Levels (DWK, JD, JA, BS).

CIG-2012-DavisonD #coordination #game studies

The huddle: Combining AI techniques to coordinate a player's game characters (TD, JD), pp. 203–210.

CIG-2013-WiensDP #behaviour #game studies #learning #scalability

Creating large numbers of game AIs by learning behavior for cooperating units (SW, JD, SP), pp. 1–8.

CIG-2015-WehrD #game studies #mining

Mining game logs to create a playbook for unit AIs (DW, JD), pp. 391–398.

CoG-2019-Eidelberg0D #game studies #performance #using

Using Active Probing by a Game Management AI to Faster Classify Players (AE, CJ0, JD), pp. 1–8.

CoG-2019-NygrenD #case study #concept #game studies #self #using

Using Simple Games to Evaluate Self-Organization Concepts: a Whack-a-mole Case Study (NN, JD), pp. 1–8.