1 / 8

HUMANOBS

HUMANOBS. Predictive Heuristics for Decision-Making in Real-World Environments. Helgi Páll Helgason, Kristinn R. Thorisson, Eric Nivel, Pei Wang Reykjavik University / Icelandic Institute for Intelligent Machines Temple University, Philadelphia. AGI 2013 - Beijing – August 2013. Problem.

mathewy
Download Presentation

HUMANOBS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. HUMANOBS Predictive Heuristics for Decision-Making in Real-World Environments Helgi Páll Helgason, Kristinn R. Thorisson, Eric Nivel, Pei Wang Reykjavik University / Icelandic Institute for Intelligent Machines Temple University, Philadelphia AGI 2013 - Beijing – August 2013

  2. Problem > Multi-objective decision making > Realistic environments: dynamic, stochastic, continuous > Insufficient knowledge and resources > Knowledge is grounded in experience and re-evaluated continuously > Reasoning under uncertainty and in real-time

  3. Problem 0.2 S0,0 0.6 S0,1 S0 0.1 S0,2 0.4 S0,3 time > Time is not discrete > Set of possible actions not always enumerable > Set of possible resulting states not always enumerable

  4. S0,0 S0,1 S0 S0,2 S0,3 time > Search guided by the predicted value of interesting future states > Value: relevancy to goals > Set of possible courses of action  ordered set of interesting actions > Interestingness derived from experience (learning, attention control and self-compilation) and current activity (goals); real valued

  5. > Predictors are controlled by success rate and confidence > Predicted state has a likelihood: likelyhood(S)=confidence(P)*(SuccessRate(P)-0.5)+0.5 > Goal’s utility: utility(G)=priority(G)*urgency(G) where urgency is the time horizon (from now), computed relatively to the horizon of all other goals > Goal’s achievement: 1 if achieved in S, -1 otherwise

  6. > Expected value for a state S derived from S0  ...  S, computed at the time of S0 ExpectedValue(S, S0)= product of all likelihoods of intermediate states leading to S from S0 * Sum of all (goal achievement in S * goal utility) > Use the expected value as the (predictive) heuristic > Domain-independence

  7. > Implemented in AERA > With an additional process of commitment (eliminate redundant goals, solve conflicts) > Scheduling of search driven by the predicted success of goals (learned from experience) in addition to the expected value of predicted future states > Anytime operation > Continuous updates of expected values as new goals are produced and new states predicted  re-scheduling

More Related