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MIIDE Overview

This document provides an in-depth overview of the MIIDE framework and its applications in knowledge management and model reasoning for Software Defined Radios (SDR) and Unmanned Aerial Vehicles (UAV). It covers context-aware capabilities, task models, user preferences, and the inference engine employed for decision-making. The MIIDE framework includes extensive knowledge about device capabilities and resource constraints, facilitating effective operation even in challenging environments. Key concepts explored include mission context, inference rules, and truth maintenance systems for dynamic and reliable evaluations.

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MIIDE Overview

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  1. MIIDE Overview 12 August 2009 MIT CSAIL

  2. System Overview Model & Knowledge Management Get context MIIDE Whiteboard (MIDOS) Context Mission Models SDR Models Preferences& constraints Model Reasoner Evaluate RadioPhysicsModels InferenceRules Results & Rationale Results & rationale Deploy and Simulate Validation results PAMS LTAE Simulator

  3. Device Model • Knowledge about Capabilities, Components and their properties • Capabilities (e.g., VideoRecording, PrecisionGuidance) • Implemented by Components • Components • Use resources (e.g., power) • Supply resources (e.g., transmission) • Have context-sensitive reliability (e.g., weather)

  4. SDR Instance VideoRecording Capability implemented by Camera Video Capture Component Components Properties Properties Hardware: yes Hardware: no Consumes Consumes Weight: 20 lbs Power: 30 watts Memory: 8 MB Reliability Weather: Clear 100% Cloudy 75% Precipitation 50% Heavy-precipitation 25%

  5. Task Model • Knowledge about the world in which devices operate • Common sense rules • User preferences • Example from UAV task • Mountainous terrain → No LOS to base • Recon & Raining → Use the HD Recon capability (Bob says it works better) • High-Power Modem → High visibility (Sam believes signals are detectable)

  6. The Inference Engine • Backward chainer with justifications and truth maintenance • Justifications • Why things are believed • Speed contradiction resolution • Can speed inference in the face of changing situations

  7. Backward Chaining • Rules motivate questions terrain type → existence of LOS to base target → terrain type • Infer high level answers from more detailed facts power & time → battery velocity & distance → time target → distance platform → velocity components → platform

  8. target locn 36.34N 68.73E Premise P1 target prot’n substantial Premise P2 Truth Maintenance System is given target loc’n 36.34N 68.73E target protection is substantial The system creates and justifies beliefs:

  9. target locn 36.34N 68.73E mountainous no los hi power modem platform visibility = high Premise P1 P1, R1 P1, R1, R2 P1, R1, R2, R3 P1, R1, R2, R3, R4 Truth Maintenance R1: locn= XX  mountainous R2: mountainous  no LOS R3: no LOS  hi power modem R4: hi power modem  platform visibility = high

  10. target protn is substantial mission risk high platform visibility = low platform visibility = high Premise P2 P2, R5 P2, R5, R6 P1, R1, R2, R3, R4 Truth Maintenance R5: protn = substantial  mission risk = high R6: mission risk = high  platform visibility = low contradiction R3: no LOS  hi power modem

  11. Storyboard

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