complacs composing learning for artificial cognitive systems year 2: specification of scenarios

CompLACSComposing Learning for Artificial Cognitive Systems

Year 2: Specification of scenarios


Cats and MouseGoal : 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 = ?


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




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




Patrolling and SurveillanceGoal : given an environment with a (predefined) set of targetlocations, 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




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 = ?


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



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



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:



Thank you!

complacs composing learning for artificial cognitive systems year 2: specification of scenarios

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