Qualitative Spatial Reasoning about Sketch

MapsKenneth Forbus, Jeffrey Usher, Vernell Champan

Represented by ilker yildirim

Introduction

• It is not useful to use CAD for early design decisions.

• Sketch maps:

express the key spatial features of a situation,

abstracting away the mass of details that would otherwise obscure the relevant aspects

Intro continues

• For computers to be able to be useful in geospatial reasoning, they need to be able to work with sketch maps.

• Qualitative spatial reasoning is essential for working with sketch maps.

• The article describes the progress they made in geospatial domain-battle reasoning.

The nuSketch Approach to Sketch Understanding

• The shortcomings of typical approaches Today’s statistical recognizers are not very

good. Today’s recognition technologies work best

within small tightly controlled domains. The assumption that sketching is a more natural

interface to legacy software.

cont’d

• Avoid recognition issues by using clever interface design.

• Focus on providing richer visual and conceptual understanding of what is sketched.

cont’d

• The key in the nuSketch approach: in human-to-human sketching, recognition facilitates communication, but recognition is not essential to it.

cont’d

• Work around the limitations of today’s recognition technologies: Use manual segmentation of ink Explicitly identify the concept that is being

depicted.Slightly reduces naturalness of nSB.

Overview of nuSketch Battlespace

• Is designed to help users develop courses of action for land forces.

• It uses a large knowledge base(CYC, DARPA, FIRE)

• nuSketch is used succesfully in several experiments (by AlphaTech and Teknowledge).

Representing Glyphs and Sketches

• The basic unit is glyph, has ink (consists of polyline) and content (conceptual entity).

• The type of the content affects the interpretation of its spatial properties.

• While some basic spatial properties of glyphs are computed, no detailed shape reasoning

cont’d

• A sketch consists of one or more subsketches, a coherent aspect of what is being sketched.

• Logically subsketches are Cyc-style microtheories, local descriptions that are internally consistent.

• Subsketches are composed of layers (friendly COA, enemy COA)

Spatial Processing of Glyphs

• The ink processor: whenever a glyph is added or changed its basic spatial properties are calculated (bb, area..)

• The vector processor is responsible for maintaining Voronoi diagrams.

Spatial Relationships b/w Glyhps

• Computing spatial relationships is crucial. Four kinds of spatial relationships

• Qualitative topological relationships

• Voronoi relationships

• Positional relationships

• Relationships based on local frames of reference.

-Qualitative Topological Relationships

• RCC8 algebra (Cohn 1996) is used.

• RCC8 is appropriate, because it captures basic distinctions (disjoint, touching, inside).

• RCC8 values are calculated for each glyph and all others in the same layer, on demand across layers values are also calculated.

-Voronoi Relationships

• Use Voronoi diagrams to compute a variety of spatial relationships.

• A voronoi diagram consists of edges that are equidistant from a pair of points.

• Again uses Dealuney triangulation, setting glyphs as points.

• Can use several diagrams to capture different notions of proximity.

-Positional Relationships

• Provide qualitative position and orientation relationship.

• Positional relationships between contents are expressed in terms of compass directions.

• A key design choice what positional relationships should be computed. Compute only pairs automatically.

-Other Frames of Reference

• Links to entities based on a local coordinate system.

• Some entities have distinct orientation, even without having a pathlike extent. (militay units have fronts..)

Position Finding

• Use knowledge of the concept of glyphs, combined with spatial reasoning on their ink, to automatically construct regions that satisfy spatial and functional criteria.

• Two important constraints in military spatial reasoning are (1) fields of fire (moutains block fire), (2) visibility (forests)

cont’d

• Position finding technique relies on polygon operations over relevant subset of glyphs.

• Depending on the constraints some are treated as obstacles.

• New regions are constructed by projections from seed locations subject to obstacle constraints.

• Multiple constraints are satisfied by combining the regions constructed for each constraint.

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cont’d

• Domain knowledge indicates the ones to be treated as obstacles.

• Consider concealment to find where enemy can be: Generate new polygons by ray casting.

• Let V be the union of those polygons

• By subtracting regions where enemy cannot be, i.e. lakes, from V to get the result.

Path Finding

• Use theory of trafficability, closer to the heuristic guidelines that we have seen used by commanders.

• Terrains are labeled as SR ‘no go’, R ‘slowly go’, UR, ‘go’.

• Those can be combined, road on the mountain is still UR.

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cont’d

• What is an obstacle can depend on the type of unit moving. (infantry/vehicles, forest/dessert)

• Label regions as SR(obstacle), R(costs more), UR(less cost)..

• Use A* search over the terrain Voroni diagram.• Once a path is found, it is smoothed to reduce its

dependencies.

Example: Hypothesizing Enemy Intent by Analogy

• Inputs are sketch of the precedent and current situations.

• Output: a new layer, which illustrates how, in the current situtation, the enemy might attempt similar to something happened in the precedent time.

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Precedent situation

• This precedent is generated by nSB in the usual way, using a template-based interface to describe why the task was succesful.

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• Your unit Bravo sees enemy unit Bait, but knows what happened to Alpha.

• Using nSB you can ask for hypothesized enemy task about the current situation depending on the precedent one.

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How the computations are made

• A key aspect is to use humanlike analogical processing for comparisons, shared similarity constraint.

• By using the cognitive simulation of analogical matching, The Structure Matching Engine (Falkenhainer, Forbur and Gentner 1989), a shared sense of similarity is achieved.

cont’d

• When intent hypothesis are requested, nSB runs SME on two descriptions, both visual and conceptual information.

• Try to catch things like (objectActedOn (:skolem Object-50) Object-64) which

means ‘there is a task that is like Object-50 (the destroy task in the precedent) that is acting on Object-64 (Bravo) in your situation

cont’d

• If such a task is found, SME is invoked again and again to complete the set of constraints.

• A critical component of the set is why the task succeeded. This explanation is used to detect whether the hypothesized task is applicable in the current situation.

cont’d

• After hypothetical task is mined from the analogy, we try to satisfy those constraints in the current situation.

• Use spatial reasoning techniques to solve these constraints and construct the appropriate positions and path.

Other Related Work and Discussion

• Winston (1982) was first to model use of precedents in supporting reasoning; our system uses a more sophisticated model of analogical reasoning.

• AI of games and nuWar being developed by the authors, constitutes the future work of nSB applications.

Questions?