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Humanization of soft computing agents |
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1 | (30) |
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1 | (1) |
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Human-centered system development framework |
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2 | (2) |
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Distributed multi-agent architecture |
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4 | (10) |
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Problem solving ontology layer |
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5 | (4) |
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9 | (2) |
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11 | (3) |
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Human-centered modelling using soft computing multi-agent architecture |
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14 | (12) |
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Human-centeredness and problem solving agent layer |
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14 | (2) |
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CRM model of Internet-banking |
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16 | (1) |
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Decomposition phase problem solving agent and CRM |
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17 | (1) |
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Control phase problem solving agent |
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18 | (1) |
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Decision phase problem solving agent |
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19 | (2) |
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Unstained cell image processing |
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21 | (4) |
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Human-centeredness and technology agent layer |
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25 | (1) |
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26 | (5) |
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27 | (4) |
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Software agents for ubiquitous computing |
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31 | (32) |
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31 | (2) |
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33 | (7) |
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33 | (1) |
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Wireless networks and roaming |
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34 | (1) |
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35 | (1) |
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Location- and context-aware services |
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36 | (1) |
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37 | (1) |
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Java -- the enabling technology for software agents |
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37 | (1) |
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38 | (2) |
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40 | (10) |
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40 | (4) |
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44 | (2) |
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46 | (1) |
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47 | (1) |
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47 | (1) |
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48 | (1) |
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49 | (1) |
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49 | (1) |
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Agent-based service provision and deployment |
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50 | (9) |
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Agent and service deployment |
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50 | (2) |
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Service partitioning based on the environment |
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52 | (2) |
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Example scenario: recommendation service |
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54 | (3) |
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57 | (2) |
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59 | (4) |
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60 | (3) |
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Agents-based knowledge logistics |
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63 | (40) |
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63 | (4) |
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KSNet-approach: major ideas |
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67 | (2) |
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Features of agent community in the system ``KSNet'' |
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69 | (5) |
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Communication, interaction and negotiation in the KL system |
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74 | (10) |
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75 | (1) |
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Constraint-based negotiation |
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76 | (1) |
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Modifications of interaction |
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77 | (1) |
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Example of utilizing constraint-based CNP |
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77 | (7) |
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Implementation of agent community |
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84 | (6) |
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Case study: health service logistics |
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90 | (3) |
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Case study: virtual supply network |
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93 | (1) |
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94 | (9) |
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96 | (1) |
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96 | (7) |
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Architectural styles and patterns for multi-agent systems |
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103 | (30) |
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103 | (3) |
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Organizational architectural styles |
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106 | (10) |
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Applying organizational styles |
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111 | (3) |
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114 | (2) |
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116 | (13) |
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117 | (1) |
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118 | (1) |
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118 | (2) |
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120 | (5) |
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125 | (1) |
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126 | (2) |
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128 | (1) |
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129 | (4) |
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130 | (3) |
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Design and behavior of a massive organization of agents |
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133 | (58) |
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133 | (3) |
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The systems with particles |
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136 | (7) |
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138 | (2) |
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Operators of determination of the behavior for an unsteady system |
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140 | (3) |
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The object approach: a very controlled process of construction and run of systems |
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143 | (5) |
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The object approach and the software engineering |
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144 | (1) |
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Objects and object-oriented design of systems |
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145 | (2) |
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Limits of the object approach |
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147 | (1) |
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Massive multi-agent systems |
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148 | (14) |
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149 | (3) |
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Nondeterminism and instability in massive multi-agent systems |
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152 | (3) |
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An agentification method for the massive multi-agent systems |
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155 | (7) |
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Analysis of the behavior of a massive agent organization: the control problem |
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162 | (18) |
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The characterization of an agent organization |
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163 | (2) |
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The morphological space, the correspondent of the space of phases for the MMAS |
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165 | (6) |
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The organization of morphological agents assuring the representation of the aspectual organization |
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171 | (4) |
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Characters of coherent groups |
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175 | (2) |
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Evocation agents and self-adaptability of the system |
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177 | (3) |
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Entropy and equation of trajectory of MMAS |
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180 | (5) |
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181 | (1) |
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Equation of trajectory: a reduction with regard to the morphological analysis |
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182 | (2) |
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Validity of the state equations |
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184 | (1) |
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Degraded forms of the state equation |
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185 | (1) |
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185 | (6) |
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187 | (4) |
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Developing agent-based applications with JADE |
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191 | (24) |
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191 | (2) |
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193 | (6) |
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193 | (3) |
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196 | (3) |
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199 | (4) |
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201 | (2) |
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203 | (3) |
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204 | (2) |
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206 | (4) |
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207 | (1) |
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208 | (2) |
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210 | (5) |
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211 | (1) |
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212 | (3) |
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A collective can do better |
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215 | (24) |
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215 | (2) |
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Insect behaviour can be inspiring |
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217 | (2) |
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219 | (6) |
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Applying real ant behaviour to computational systems |
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219 | (3) |
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Solving classic optimisation problems |
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222 | (1) |
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Telecommunications applications |
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223 | (1) |
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Robot navigation applications |
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224 | (1) |
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Other robotic applications |
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224 | (1) |
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Image processing applications |
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225 | (1) |
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Combining reinforcement learning and synthetic pheromones |
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225 | (7) |
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225 | (1) |
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Synthetic pheromones and Q-learning |
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226 | (6) |
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Cooperative robotic transport |
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232 | (1) |
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232 | (7) |
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233 | (6) |
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Coordinating multi-agent assistants with an application by means of computational reflection |
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239 | (40) |
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240 | (3) |
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The motivation for a multi-assistant architecture |
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243 | (3) |
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Example: extending a Web browser with assistant agents |
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244 | (2) |
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The multi-agent reflective architecture |
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246 | (18) |
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247 | (1) |
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248 | (3) |
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251 | (2) |
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253 | (4) |
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Coordinator-assistants interactions |
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257 | (2) |
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A concrete example: an assistant that highlights keywords for a Web browser |
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259 | (5) |
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A case study: e-commerce assistants for a Web browser |
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264 | (8) |
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265 | (3) |
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Data extraction assistant |
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268 | (2) |
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270 | (2) |
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272 | (7) |
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274 | (5) |
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Learning by exchanging advice |
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279 | (36) |
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279 | (2) |
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Communicating to improve learning: historical notes and review |
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281 | (2) |
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Early work on exchange of information during learning |
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281 | (1) |
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282 | (1) |
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283 | (9) |
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Exchanging information during learning |
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283 | (1) |
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What type of information? |
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284 | (1) |
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How to integrate this information with the usual learning process? |
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285 | (1) |
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When should an agent request/accept information? |
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285 | (5) |
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Where to get information? |
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290 | (2) |
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292 | (12) |
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294 | (4) |
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298 | (3) |
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301 | (3) |
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304 | (5) |
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304 | (3) |
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307 | (2) |
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Conclusions and future work |
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309 | (6) |
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311 | (1) |
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311 | (4) |
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Adaptation and mutation in multi-agent systems and beyond |
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315 | (40) |
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315 | (2) |
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317 | (8) |
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319 | (1) |
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319 | (1) |
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The amplitude of the change: weak vs. strong mutability |
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320 | (1) |
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The granularity of mutation |
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321 | (1) |
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The continuity of interactions: runtime vs. stoptime |
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322 | (1) |
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The initiator of the mutation |
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322 | (1) |
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323 | (1) |
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324 | (1) |
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Other classification approaches |
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325 | (1) |
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A formal description of mutability |
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325 | (11) |
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Agent models and mutability |
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325 | (3) |
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A multiplane state machine model of agent behavior |
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328 | (1) |
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329 | (1) |
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Decomposition in the plane. expressing ``change'' |
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330 | (1) |
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331 | (1) |
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Mutation operators and invariance properties |
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332 | (2) |
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How useful are the invariance properties? |
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334 | (2) |
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A software engineering perspective on adaptive and mutable agents |
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336 | (11) |
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Adding new functionality to the agent |
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338 | (3) |
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Removing functionality from an agent |
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341 | (2) |
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Adapting to new requirements |
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343 | (3) |
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Splitting and merging agents |
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346 | (1) |
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347 | (8) |
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349 | (6) |
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Intelligent action acquisition for animated learning agents |
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355 | (32) |
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355 | (2) |
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Current state of the art in automatic character animation |
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357 | (6) |
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General animation architectures |
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357 | (4) |
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Physics-based controllers |
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361 | (1) |
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362 | (1) |
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Overview of other concepts |
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363 | (4) |
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363 | (1) |
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The agent's senses: collision detection and avoiding |
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364 | (2) |
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366 | (1) |
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Implementation of the Q-learning |
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367 | (3) |
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System implementation and results |
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370 | (8) |
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370 | (2) |
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372 | (6) |
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378 | (1) |
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378 | (9) |
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380 | (1) |
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380 | (7) |
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Using stationary and mobile agents for information retrieval and e-commerce |
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387 | (59) |
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Basic concepts and background |
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388 | (8) |
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Agent and multi-agent systems |
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388 | (2) |
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Cooperation and communication mechanisms |
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390 | (1) |
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Communication among agents |
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390 | (2) |
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392 | (2) |
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Mobile agent and mobile code |
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394 | (2) |
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Multi-agent architecture for information retrieval |
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396 | (15) |
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Mobile agent information retrieval |
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397 | (2) |
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Characterization of the architecture |
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399 | (9) |
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Experimental number application |
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408 | (1) |
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Principles of the application |
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408 | (1) |
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Design choices and modifications to the initial application |
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409 | (1) |
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Internet picture retrieval application |
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410 | (1) |
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Implementation of the information retrieval architecture |
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411 | (8) |
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Generic classes and interfaces |
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411 | (3) |
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414 | (3) |
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Implementation and testing environment |
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417 | (2) |
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Evaluation of the information retrieval architecture |
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419 | (10) |
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419 | (3) |
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Information retrieval scenarios |
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422 | (7) |
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Multi-agent architecture for product retrieval |
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429 | (15) |
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Description of the problem and general scenario |
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429 | (2) |
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Solution and suggested algorithms |
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431 | (2) |
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Architecture and agent structure |
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433 | (3) |
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Implementation and performance evaluation |
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436 | (8) |
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444 | (2) |
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446 | |