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The Art of Anticipation: Integrating Analogical Reasoning with Perception

This project explores anticipation mechanisms, explicit & implicit anticipation through analogy-making and context, and the integration of these processes for guiding attention in reasoning and perception. Examples illustrate how anticipation based on memory reconstruction, analogy-making, and context can be applied in various scenarios. The implementation tool AMBR is introduced as a cognitive model for analogy-making, with hybrid symbolic processing and connectionist activation. Challenges to the pre-existing version of AMBR and scenario implementation for simulation experiments are discussed. The study focuses on scenarios like finding objects in rooms or mazes, guards and thieves. The development of simulation tools involves re-implementing the AMBR model in C# and testing software compatibility for AIBO and Pioneer 3 robots in the WEBOTS 5 simulation environment.

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The Art of Anticipation: Integrating Analogical Reasoning with Perception

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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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