A Cognitive Map for an Artificial Agent

transcript

Unmesh KurupRPI

kurupu@rpi.edu

B. ChandrasekaranThe Ohio State University

chandra@cse.ohio-state.edu

Overview

• Cognitive map – Features– Goals

• biSoar architecture– Cognitive Map in biSoar– Examples

Cognitive Map

• Representation of large-scale space

Cognitive Map

• Representation of large-scale space– Layout of a city

Cognitive Map

• Representation of large-scale space– Layout of a city or a building

Cognitive Map

• Supports a number of problem solving tasks.

Cognitive Map

• Supports a number of problem solving tasks.– Route-finding: How can I get to the Radisson from

here? • Exit the hotel, Take a left, Right at 23rd St, Right on Clark.

Cognitive Map

• Supports a number of problem solving tasks.– Geo Recall: Is your hotel to the west of this hotel?

Cognitive Map

• Supports a number of problem solving tasks.– Finding shortcuts: Is there a shorter way to my

hotel?• Possible: Take a right on 20th st.

Features of the Cognitive Map

• Non-holistic

If you take a left on Crystal Dr, you will get to the intersection of Crystal Dr and 23rd St

If you take a right on 23rd St, you will get to the intersection of 23rd St and Clark.

• Non-holistic– manageable

• Non-holistic– Manageable

If you take a right on Clark you will get to your hotel

• Non-holistic– Manageable, updateable,

If you take a right on 20th St, you will get to the intersection of 20th St and Clark.

New info? Just add it!

• Non-holistic– Manageable, updateable, composable

If you take a right on 20th St, you will get to the intersection of 20th St and Clark.

• Has both symbolic and metric aspects

• Capture features– A non–holistic representation with both

symbolic and metric aspects

• Capture features– A non–holistic representation with both

symbolic and metric aspects

• Cognitive architecture approach

biSoar

A Bimodal Cognitive Architecture

biSoar

• Soar + Diagrammatic Representation System (DRS)

DRS - Diagrammatic Representation System (Chandra et. al. 2004)

• Diagrams consist of three types of objects – Points, Curves & Regions.

DRS - Diagrammatic Representation System (Chandra et. al. 2004)

• Perceptual routines allow extraction of relationships between objects in the diagram.– Ex: LeftOf, RightOf etc

• Action routines allow the diagram to be modified– AddPoint, AddCurve etc

biSoar

Symbolic component:Block (A), Block (B), Block (C), On (A,B), On (B,C)

Selected Operator: None

Working Memory

Diagrammatic component

Soar WM DRS

Working Memory:

Block (A), Block (B), Block (C), On (A,B), On (B,C)

Selected Operator: None

biSoar

LTM and Learning in biSoar

• No change to LHS of LTM rules in biSoar• RHS can extract information from or modify

diagrammatic component as well.– If a and b are clear and goal is on(a,b) then

translate(a on b)

• Chunking in the bimodal case is straightforward.

biSoar

• Soar + Diagrammatic Representation System (DRS)

• biSoar does not do– Any sort of image processing– Object recognition

• Assumes– a diagrammatic representation (DRS form) of

the input is available.

Representing LSS in biSoar

If goal is find_next_location and curr location is x and traveling in direction Dx on route Rx, then

destination is location y, diagram is DRSx

Representing LSS in biSoar

If goal is find_next_location and curr location is A and traveling in direction Dx on route Rx, then

destination is location B, diagram is DRSx

If goal is find_next_location and curr location is R2R5 and traveling Right on Route R2, then destination is P2, diagram is DRS1

Examples – Route-finding

• Given a map, find route from P1 to P2Route-finding Strategy• locate the starting & destination locations

in the map• make the starting location the current

location• Find the routes on which the current

location lies• For each route, find the directions of travel• for each route and direction of travel, find

the next location • calculate the Euclidean distance between

these new locations and the destinations• pick the location that is closest to the

destination and make that the current point

• repeat 3-8 until destination is reached

Learning while route-finding

Example rules learned during wayfinding

Within-task transfer

• Task1 – P1 to R3R5

• Task1 – P1 to R3R5, Task2 – P4 to P2

• Task1 – P1 to R3R5, Task2 – P4 to P2• New route-finding task – P4 to R3R5

Between-task transfer

• Geographic Recall problem• What’s the spatial relation between R1R3 and

Finding short-cuts

If goal is find_routes and at R2R5 then there is a route r5 in the up direction.

Finding short-cuts

Find route from P2 to P5

Finding short-cuts

Find route from P2 to P5

Conclusion

• biSoar’s CM (representation of LSS)– Is non-holistic– has metric and non-metric information– Can be used to solve a variety of tasks

involving LSS.– Supports learning and transfer of learned

information within and between tasks.

Thanks!

A Cognitive Map for an Artificial Agent

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