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Changes between Version 73 and Version 74 of OpponentModels
- Timestamp:
- 04/24/11 16:20:08 (14 years ago)
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OpponentModels
v73 v74 7 7 ||'''Title'''||A Framework for Building Intelligent SLA Negotiation Strategies under Time Constraints|| 8 8 ||'''Author(s)'''||G.C. Silaghi, L.D. Şerban and C.M. Litan|| 9 ||'''Cited'''||-|| 9 10 ||'''Subject(s)'''|||| 10 11 ||'''Summary'''|||| … … 16 17 ||'''Title'''||A Framework for Multi-agent Electronic Marketplaces: Analysis and Classification of Existing Systems || 17 18 ||'''Author(s)'''||K. Kurbel and I. Loutchko|| 19 ||'''Cited'''||25|| 18 20 ||'''Subject(s)'''|||| 19 21 ||'''Summary'''|||| … … 26 28 ||'''Title'''||AgentFSEGA - Time Constrained Reasoning Model for Bilateral Multi-Issue Negotiation|| 27 29 ||'''Author(s)'''||L.D. Serban, G.C. Silaghi, and C.M. Litan|| 30 ||'''Cited'''||-|| 28 31 ||'''Subject(s)'''|||| 29 32 ||'''Summary'''|||| … … 35 38 ||'''Title'''||An Architecture for Negotiating Agents that Learn|| 36 39 ||'''Author(s)'''||H.H. Bui, S. Venkatesh, and D. Kieronska|| 40 ||'''Cited'''||2|| 37 41 ||'''Subject(s)'''|||| 38 42 ||'''Summary'''|||| … … 44 48 ||'''Title'''||Analysis of Negotiation Dynamics|| 45 49 ||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov|| 50 ||'''Cited'''||5|| 46 51 ||'''Subject(s)'''|||| 47 52 ||'''Summary'''|||| … … 53 58 ||'''Title'''||Anticipating Agent's Negotiation Strategies in an E-marketplace Using Belief Models|| 54 59 ||'''Author(s)'''||F. Teuteberg, K. Kurbel|| 60 ||'''Cited'''||11|| 55 61 ||'''Subject(s)'''|||| 56 62 ||'''Summary'''|||| … … 62 68 ||'''Title'''||Bayesian Learning in Bilateral Multi-issue Negotiation and its Application in MAS-based Electronic Commerce|| 63 69 ||'''Author(s)'''||J. Li, Y. Cao|| 70 ||'''Cited'''||6|| 64 71 ||'''Subject(s)'''|||| 65 72 ||'''Summary'''|||| … … 71 78 ||'''Title'''||Bayesian Learning in Negotiation|| 72 79 ||'''Author(s)'''||D. Zeng, K. Sycara|| 80 ||'''Cited'''||355|| 73 81 ||'''Subject(s)'''|||| 74 82 ||'''Summary'''|||| … … 80 88 ||'''Title'''||Benefits of Learning in Negotiation|| 81 89 ||'''Author(s)'''||D. Zeng, K. Sycara|| 90 ||'''Cited'''||116|| 82 91 ||'''Subject(s)'''||Benefits of learning, Bayesian learning, reservation values|| 83 92 ||'''Summary'''|| Growing interest in e-commerce motivates research in automated negotiation. Building intelligent negotiation agents is still[[br]] emerging. In contrast to most negotiation models, sequential decision model allows for learning. Learning can help understand[[br]] human behaviour, but can also result in better results for the learning party. Bayesian learning of reservation[[br]] values can be used to determine the zone of agreement for an issue based on the domain knowledge and bidding interactions.[[br]] Concluding for one-issue, learning positively influences bargaining quality, number of exchanged proposals,[[br]] and leads to a better compromise if both learn. Learning works always works better in the proposed case.|| … … 89 98 ||'''Title'''||Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent|| 90 99 ||'''Author(s)'''||C. Mudgal, J. Vassileva|| 100 ||'''Cited'''||42|| 91 101 ||'''Subject(s)'''|||| 92 102 ||'''Summary'''|||| … … 98 108 ||'''Title'''||Compromising Strategy based on Estimated Maximum Utility for Automated Negotiating Agents|| 99 109 ||'''Author(s)'''||S. Kawaguchi, K. Fujita, T. Ito|| 110 ||'''Cited'''||-|| 100 111 ||'''Subject(s)'''|||| 101 112 ||'''Summary'''|||| … … 106 117 107 118 ||'''Title'''||Facing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling|| 108 ||'''Author(s)'''||J. Li, Y. Cao|| 119 ||'''Author(s)'''||Y. Oshrat, R. Lin, S. Kraus|| 120 ||'''Cited'''||19|| 109 121 ||'''Subject(s)'''|||| 110 122 ||'''Summary'''|||| … … 116 128 ||'''Title'''||IAMhaggler: A Negotiation Agent for Complex Environments|| 117 129 ||'''Author(s)'''||C.R. Williams, V. Robu, E.H. Gerding, and N.R. Jennings|| 130 ||'''Cited'''||-|| 118 131 ||'''Subject(s)'''|||| 119 132 ||'''Summary'''|||| … … 125 138 ||'''Title'''||Inferring implicit preferences from negotiation actions|| 126 139 ||'''Author(s)'''||A. Restificar and P. Haddawy|| 140 ||'''Cited'''||10|| 127 141 ||'''Subject(s)'''|||| 128 142 ||'''Summary'''|||| … … 134 148 ||'''Title'''||Integration of Learning, Situational Power and Goal Constraints Into Time-Dependent Electronic Negotiation Agents|| 135 149 ||'''Author(s)'''||W.W.H. Mok|| 150 ||'''Cited'''||-|| 136 151 ||'''Subject(s)'''|||| 137 152 ||'''Summary'''|||| … … 143 158 ||'''Title'''||Learning Algorithms for Single-instance Electronic Negotiations using the Time-dependent Behavioral Tactic|| 144 159 ||'''Author(s)'''||W.W.H Mok and R.P. Sundarraj|| 160 ||'''Cited'''||17|| 145 161 ||'''Subject(s)'''|||| 146 162 ||'''Summary'''|||| … … 152 168 ||'''Title'''||Learning an Agent's Utility Function by Observing Behavior|| 153 169 ||'''Author(s)'''||U. Chajewska, D. Koller, D. Ormoneit|| 170 ||'''Cited'''||54|| 154 171 ||'''Subject(s)'''|||| 155 172 ||'''Summary'''|||| … … 162 179 ||'''Title'''||Learning an Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs|| 163 180 ||'''Author(s)'''||R.M. Coehoorn, N.R. Jennings|| 181 ||'''Cited'''||78|| 164 182 ||'''Subject(s)'''||KDE Learning, Negotiation model, Concession based strategy|| 165 183 ||'''Summary'''|| Effective and efficient multi-issue negotiation requires an agent to have some indication of it's opponent's preferences [[br]]over the issues in the domain. Kernel Density Estimation (KDE) is used to estimate the weight attached to different issues [[br]]by different agents. It is assumed that if the value of an issue increases, that this is positive for one agent, and negative [[br]]for the other. No assumptions about relation between time, negotiation history and issue-weight are required, in contrast [[br]]to Bayesian learning. The difference between concessive (counter)offers is used to estimate the weights of the issues [[br]] (assumption: stronger concessions are made later on in the negotiation). Faratin's hill climbing algorithm augmented with KDE is [[br]]used to propose the next bid. KDE proved succesful on the used negotiation model. Future works entails testing the approach [[br]]against different opponent strategies and extending the approach to other negotiation models (see assumption in summary). || … … 171 189 ||'''Title'''||Learning Opponents' Preferences in Multi-Object Automated Negotiation|| 172 190 ||'''Author(s)'''||S. Buffett and B. Spencer|| 191 ||'''Cited'''||18|| 173 192 ||'''Subject(s)'''|||| 174 193 ||'''Summary'''|||| … … 180 199 ||'''Title'''||Learning other Agents' Preferences in Multiagent Negotiation using the Bayesian Classifier.|| 181 200 ||'''Author(s)'''||H.H. Bui, D. Kieronska, S. Venkatesh|| 201 ||'''Cited'''||29|| 182 202 ||'''Subject(s)'''|||| 183 203 ||'''Summary'''|||| … … 189 209 ||'''Title'''||Modeling Opponent Decision in Repeated One-shot Negotiations|| 190 210 ||'''Author(s)'''||S.Saha, A. Biswas, S. Sen|| 211 ||'''Cited'''||26|| 191 212 ||'''Subject(s)'''|||| 192 213 ||'''Summary'''|||| … … 198 219 ||'''Title'''||Negotiation Decision Functions for Autonomous Agent|| 199 220 ||'''Author(s)'''||P. Faratin, C. Sierra, N.R. Jennings|| 221 ||'''Cited'''||718|| 200 222 ||'''Subject(s)'''|||| 201 223 ||'''Summary'''|||| … … 207 229 ||'''Title'''||Negotiation Dynamics: Analysis, Concession Tactics, and Outcomes|| 208 230 ||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov|| 231 ||'''Cited'''||7|| 209 232 ||'''Subject(s)'''|||| 210 233 ||'''Summary'''|||| … … 216 239 ||'''Title'''||On-Line Incremental Learning in Bilateral Multi-Issue Negotiation|| 217 240 ||'''Author(s)'''||V. Soo, C. Hung|| 241 ||'''Cited'''||18|| 218 242 ||'''Subject(s)'''|||| 219 243 ||'''Summary'''|||| … … 225 249 ||'''Title'''||Opponent Model Estimation in Bilateral Multi-issue Negotiation|| 226 250 ||'''Author(s)'''||N. van Galen Last|| 251 ||'''Cited'''||-|| 227 252 ||'''Subject(s)'''|||| 228 253 ||'''Summary'''|||| … … 234 259 ||'''Title'''||Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning|| 235 260 ||'''Author(s)'''||K. Hindriks, D. Tykhonov|| 261 ||'''Cited'''||33|| 236 262 ||'''Subject(s)'''|||| 237 263 ||'''Summary'''|||| … … 243 269 ||'''Title'''||Optimal negotiation strategies for agents with incomplete information|| 244 270 ||'''Author(s)'''||S.S. Fatima, M. Wooldridge and N.R. Jennings|| 271 ||'''Cited'''||88|| 245 272 ||'''Subject(s)'''|||| 246 273 ||'''Summary'''|||| … … 253 280 ||'''Title'''||The Benefits of Opponent Models in Negotiation|| 254 281 ||'''Author(s)'''||K. Hindriks, C.M. Jonker, D. Tykhonov|| 282 ||'''Cited'''||-|| 255 283 ||'''Subject(s)'''|||| 256 284 ||'''Summary'''|||| … … 262 290 ||'''Title'''||The First Automated Negotiating Agents Competition (ANAC 2010)|| 263 291 ||'''Author(s)'''||T. Baarslag, K. Hindriks, C. Jonker, S. Kraus, R. Lin|| 292 ||'''Cited'''||-|| 264 293 ||'''Subject(s)'''||ANAC, overview multiple agents, opponent models, acceptance conditions|| 265 294 ||'''Summary'''||The ANAC competition models bilateral multi-issue closed negotiations and provides a benchmark for negotiation agents. [[br]]Opponent models can also be used to identify the type of strategy of the opponent. Interesting agents for further analysis [[br]]are: IAM(crazy)Haggler, FSEGA (profile learning), and Agent Smith. Issues can be predicatable, which means that they [[br]]have a logical order, or unpredicatable, such as colors. This paper also includes acceptance conditions.|| … … 271 300 ||'''Title'''||Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation|| 272 301 ||'''Author(s)'''||K.V. Hindriks and D. Tykhonov|| 302 ||'''Cited'''||6|| 273 303 ||'''Subject(s)'''|||| 274 304 ||'''Summary'''|||| … … 280 310 ||'''Title'''||Using Similarity Criteria to Make Issue Trade-offs in Automated Negotiations|| 281 311 ||'''Author(s)'''||P. Faratin, C. Sierra, N.R. Jennings|| 312 ||'''Cited'''||367|| 282 313 ||'''Subject(s)'''|||| 283 314 ||'''Summary'''|||| … … 289 320 ||'''Title'''||Yushu: a Heuristic-Based Agent for Automated Negotiating Competition|| 290 321 ||'''Author(s)'''||B. An and V. Lesser|| 322 ||'''Cited'''||-|| 291 323 ||'''Subject(s)'''|||| 292 324 ||'''Summary'''||||
