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Challenge

Google ADK Multimodal Casino Floor Traffic & Revenue Optimization

Build a Google ADK multimodal agent using Gemini to analyze floor plan imagery and IoT sensor streams for regional casino resorts. The system identifies high-conversion non-gaming revenue layouts and predicts spatial yield optimization with 88% precision.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Uses Google Agent Development Kit (ADK) and Gemini multimodal capabilities to process floor layout diagrams and real-time foot-traffic heatmaps for non-gaming resort revenue optimization.

Delivery guide

How work is evaluated

Evaluation

Evaluates multimodal layout parsing and non-gaming revenue prediction accuracy.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Revenue Metric Bounds

Verifies that projected revenue lift values are physically realistic (0-50%)

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Layout Recommendation Relevance

Expert rating score on space reallocation suggestions • target: 0.88 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement Google ADK multimodal processing agents leveraging Gemini APIs

  • Process visual floor plans alongside tabular spatial foot-traffic sensors

  • Identify optimal dining, retail, and entertainment layout placements

  • Predict non-gaming square-foot yield optimizations

Resources and assets

Reference links and supporting material

Dataset notes

20 regional casino floor plans with paired foot-traffic heatmaps and venue yield metrics.

Challenge setup

Evaluates multimodal layout parsing and non-gaming revenue prediction accuracy.

Challenge input

JSON containing layout_image_uri, hourly_foot_traffic_count, and current_non_gaming_sqft

Google ADK

Provides direct multimodal integration with Gemini and Vertex AI.

Evaluated output

JSON containing recommended_reallocation, projected_revenue_lift_pct, and bottleneck_zones

Evaluation checks
  • Verifies that projected revenue lift values are physically realistic (0-50%)
  • Expert rating score on space reallocation suggestions • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: MultimodalSpatialYield-v1
  • Layout Recommendation Relevance target: 0.88
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Environment
  • Modal · Optional
Action Space
  • Google ADK · Required
  • Google · Optional
Policy Serving
  • Modal · Optional

Evaluation contract

  • Revenue Metric Bounds · Weight 1
  • Layout Recommendation Relevance · Weight 1

Metrics

  • Layout Recommendation Relevance · Target 0.88

Benchmarks

  • MultimodalSpatialYield-v1
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing layout_image_uri, hourly_foot_traffic_count, and current_non_gaming_sqft

    Output: JSON containing recommended_reallocation, projected_revenue_lift_pct, and bottleneck_zones

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Development Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Google ADK Multimodal Casino Floor Traffic & Revenue Optimization