1. CompLACS Composing Learning for Artificial Cognitive Systems Year 2: Specification of scenarios

2. CompLACS Composing Learning for Artificial Cognitive Systems Cats and Mouse Goal : cats cooperate to maintain the mouse in a given area, then capture it. Wanted: algorithm to control cats case 1: each cat has access to the full noiseless state (centralised) case 2: as above but noisy (centralised) case 3: each agent has to learn a model of other cats (distributed) case 4: propose your “variation” method 1 + method 2 = ?

3. CompLACS Composing Learning for Artificial Cognitive Systems Active Vision Search Goal : find item of interest in large environment from visual data (on board camera). Wanted: algorithm to perform “optimal” search case 1: single helicopter case 2: multi-helicopter case 3: propose your “variation” method 1 + method 2 = ?

4. CompLACS Composing Learning for Artificial Cognitive Systems Plume Distribution Estimation Goal : given helicopters with on-board sensors, estimate distribution (in space) of a property (e.g. CO concentration). Wanted: representation for distribution and algorithm for active sampling case 1: single helicopter, static distribution (simulation) case 2: multiple helicopters, static distribution case 3: time varying distribution/wind multiple helicopters case 4: propose your “variation” method 1 + method 2 = ?

5. CompLACS Composing Learning for Artificial Cognitive Systems Patrolling and Surveillance Goal : given an environment with a (predefined) set of target locations, have a team of helicopters patrolling the environment in an "effective" way so as to prevent attacks from intruders. Wanted: learn how to patrol the targets so as to minimize the chance for an intruder to attack a target. case 1: single helicopter, known targets, unknown dynamics of the environment (travelling time from one location to the other) case 2: team of helicopters, introduce a cost function for patrolling case 3: propose your “variation” method 1 + method 2 = ?

6. CompLACS Composing Learning for Artificial Cognitive Systems Feed Evaluation Goal : Given large set of RSS feeds, find most "relevant" where relevance is a linear function of their content. Relevance is approximated by using feed-features. Wanted: Sublinear time solution: can not explore all feeds case 1: Static list of feeds / static reward function / stationary distribution in each feed case 2: Reward / Contents may drift case 3: Features chosen by us vs. features learned by system case 4: Propose your tools Method1 + Method 2 = ?

7. CompLACS Composing Learning for Artificial Cognitive Systems Search URLs Goal : Generate vast list of feeds. Given graph (hypertext) find nodes corresponding to RSS feeds, using the contents of the webpages. Wanted: Algorithm to make decisions to focused search case 1: Learn value function, as function of content. case 2: Value function is contextual: input parametrized by some extra features case 3: Propose your tools Method1 + Method 2 = ?

8. CompLACS Composing Learning for Artificial Cognitive Systems Learning Representation of Articles Goal : Learn a low-dimensional vector to embed documents optimally for certain tasks. Wanted: Method to extract a vector representation of an article with some optimum property for another module to use. case 1: Features for a single classification task case 2: Features for a set of classification tasks case 3: Lossless vs. lossy representations case 4: Most-general representation for a fixed budget case 5: Propose your tools Method1 + Method 2 = ?

9. CompLACS Composing Learning for Artificial Cognitive Systems Optimize non-convex function Goal : Exploit non-convex function maximization / peak finding for standard ML algorithms Wanted: Method to cast them as find all-maxima of a function case 1: EM case 2: Ensemble classifiers case 3: Clustering case 4: Method1 + Method 2 = ?

10. CompLACS Composing Learning for Artificial Cognitive Systems Thank you!