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Anticipation by Analogy

Anticipation by Analogy. An Attempt to Integrate Analogical Reasoning with Perception, Selective Attention, Context, and Motor Control. Anticipation Mechanisms. Explicit Anticipation: analogy-making Predictions based on one single example Implicit Anticipation: context & relevance

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Anticipation by Analogy

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  1. Anticipation by Analogy An Attempt to Integrate Analogical Reasoning with Perception, Selective Attention, Context, and Motor Control MindRACES, First Review Meeting, Lund, 11/01/2006

  2. Anticipation Mechanisms • Explicit Anticipation: analogy-making • Predictions based on one single example • Implicit Anticipation: context & relevance • Predicting relevance based on context – guiding attention in reasoning and perception • Combining Explicit and Implicit Anticipation MindRACES, First Review Meeting, Lund, 11/01/2006

  3. Examples of Anticipation based on analogy-making and context • Searching for your keys They are not at their usual place, so • try to reconstruct what you have done with them (memory reconstruction), • reminding of old episodes of key search and where you found them (analogy) • Perceived elements (context) guide the reconstruction, reminding, and analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  4. Examples of Anticipation based on analogy-making and context • Searching for your car in the parking slot • try to reconstruct where you have parked it (memory reconstruction), • reminding of old episodes of car search and where you found it (analogy) • reminding of old episodes of key search and where you found it (remoteanalogy) • Perceived elements (context) guide the reconstruction, reminding, and analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  5. Examples of Anticipation based on analogy-making and context • Predicting the outcome of a game • The same as the last outcome • The same as the last failure • The same as the last success • The same as an special old case with this game • The same as an old case with another game • Perceived elements (context) guide the reminding and analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  6. Examples of Anticipation based on analogy-making and context • Predicting your partner’s or your rival’s next move • What would I do in this situation (analogy with myself) • What has this partner/rival done is analogous situation in the past (reminding of specific old case) • What has another partner/rival done is analogous situation in the past (reminding of specific old case) • Perceived elements (context) guide the reminding and analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  7. Analogy-Making • Analogy-making is the transfer of a system of relations from one domain (base) to another (target). Similarity based on structure, not overall similarity. • Analogy is a very basic human ability. MindRACES, First Review Meeting, Lund, 11/01/2006

  8. Analogy-Making water milkw corr-to in in corr-to corr-to tpot tpot in on corr-to oven hplate corr-to MindRACES, First Review Meeting, Lund, 11/01/2006

  9. Sun Rutherford’s Analogy Nucleus ++ - - MindRACES, First Review Meeting, Lund, 11/01/2006

  10. Rutherford’s analogy The hydrogen atom is like our solar system. The Sun has a greater mass than the Earth and attracts it, causing the Earth to revolve around the Sun. The nucleus also has a greater mass then the electron and attracts it. Therefore it is plausible that the electron also revolves around the nucleus. MindRACES, First Review Meeting, Lund, 11/01/2006

  11. Main Implementation Tool - AMBR • AMBR – a cognitive model of human analogy-making. • The model is hybrid and integratessymbolic processing and connectionist spreading activation and constraint satisfaction at a micro level. • The model is highly parallel and the behavior of the macro system emerges from the local interactions of micro agents. MindRACES, First Review Meeting, Lund, 11/01/2006

  12. Challenges to the pre-existing version of AMBR • AMBR was a theoretical tool – it was never applied in realistic domain before. • AMBR was developed for complex problem-solving, not for anticipation. • AMBR was a model of the mind outside of a body – no interactions with the environment – no perception, no manipulation. • AMBR was coded in LISP with no possibilities for communications with other software. MindRACES, First Review Meeting, Lund, 11/01/2006

  13. Scenario Implementation • Selection of the scenarios to be used by NBU • Developing simulation tools • First simulation experiments MindRACES, First Review Meeting, Lund, 11/01/2006

  14. Scenarios studied by NBU • Finding and Looking for an object (finding an object in a single room or in a maze of multiple rooms) • Guards and thieves (collecting objects which are guarded by other agents) MindRACES, First Review Meeting, Lund, 11/01/2006

  15. Rooms layout MindRACES, First Review Meeting, Lund, 11/01/2006

  16. Looking for an Object (Scenario 1) MindRACES, First Review Meeting, Lund, 11/01/2006

  17. Guards and thieves (Scenario 3) MindRACES, First Review Meeting, Lund, 11/01/2006

  18. Developing Simulation Tools • The AMBR model is being further developed and re-implemented in C#. • The software for AIBO and Pioneer 3 is being mastered and tested. • The simulation environment WEBOTS 5 is studied and simple simulation of the scenarios are being built. • A middle tier is being implemented for communication between AMBR on one side and the robots and simulated environment on the other. MindRACES, First Review Meeting, Lund, 11/01/2006

  19. Overall System Architecture WORLD COMMUNI-CATION REASONING MindRACES, First Review Meeting, Lund, 11/01/2006

  20. World AIBO ERS7 Webots simulation MindRACES, First Review Meeting, Lund, 11/01/2006

  21. Communication • World tier -> Reasoning tier • Collect information about the world using symbolic data from Webots • Report it to the Reasoning layer in suitable for AMBR form • Reasoning tier -> World tier • Get the motion plan from AMBR: e.g “Go to the left cube” • Send commands for movement to Webots turning in place, walking forward MindRACES, First Review Meeting, Lund, 11/01/2006

  22. Reasoning • Reasoning by analogy with previous episode (using the AMBR cognitive model) • Describing AMBR in UML • Implementation of the AMBR model in C# • Project infrastructure (version control, unit testing, etc.) MindRACES, First Review Meeting, Lund, 11/01/2006

  23. Anticipation by Analogy ? MindRACES, First Review Meeting, Lund, 11/01/2006

  24. Past Episodes in Robot’s Memory Target situation MindRACES, First Review Meeting, Lund, 11/01/2006

  25. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  26. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  27. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  28. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  29. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  30. Results from the Simulation of Anticipation by Analogy MindRACES, First Review Meeting, Lund, 11/01/2006

  31. Simulation Result - Video MindRACES, First Review Meeting, Lund, 11/01/2006

  32. Challenges and Problems • AMBR was developed as a model of complex analogies and therefore fitting and changes were required to produce anticipation: • Superficial features such as colors are typically ignored – colors are important in this domain; • Episodes are complex and differ significantly from each other – episodes are very similar in this domain. MindRACES, First Review Meeting, Lund, 11/01/2006

  33. Challenges and Problems • AMBR was developed as an isolated reasoning model – needs to be integrated into a complete cognitive system: • Perceptual abilities need to be integrated that will encode the target situation – perception of objects, properties and relations – this is solved in the simulation environment, needs to be solved with real robots; integration of symbolic and sub-symbolic approach; • Selective attention needs to be modeled to limit the representation of the target and to focus on certain aspects of the situation; • Motor control – planning and motor control mechanisms MindRACES, First Review Meeting, Lund, 11/01/2006

  34. Challenges and Problems • The simulation results need to be compared and possibly fitted to human data: • Some of the simulation data perfectly match human data; • Some differ significantly. MindRACES, First Review Meeting, Lund, 11/01/2006

  35. Comparing Simulation and Human Data: 100 Runs on each Target MindRACES, First Review Meeting, Lund, 11/01/2006

  36. Comparing Simulation and Human Data: 100 Runs on each Target Simulation data Human data MindRACES, First Review Meeting, Lund, 11/01/2006

  37. Integration with work of other partners • Perception of objects, properties, relations – cooperation with IDSIA, LUCS, ISTC, OFAI • Selective attention – integration of top-down and bottom-up mechanisms – cooperation with LUCS, IDSIA • Emotions as regulators of the mechanisms of analogy-making, analogies as source of emotions – cooperation with ISTC, IST MindRACES, First Review Meeting, Lund, 11/01/2006

  38. Anticipation by Analogy: Putting things together Emotions (IST, ISTC) Perception: target representation: IDSIA, LUCS Analogical reasoning (NBU) Selective attention: LUCS, IDSIA, NBU Motor control: OFAI, IDSIA, LUCS MindRACES, First Review Meeting, Lund, 11/01/2006

  39. ? Thank you for your attention! MindRACES, First Review Meeting, Lund, 11/01/2006

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