Behaviour Tree
ScoutBrain uses selector/sequence trees. Higher-risk branches pre-empt lower ones each tick - predator danger can interrupt food search.
Academic case study · CS6P05ES · Game development
Lost Boy Scout Adventure
A playable Unity 2D intelligent-agent survival game. The AI scout manages hunger, water, and energy under partial observability - behaviour trees, perception, and memory - not a fixed cutscene script.
Direct download of the Windows standalone build. Extract and run LostBoyScoutAdventure.exe beside UnityPlayer.dll and the data folder. Executable: LostBoyScoutAdventure.exe.
Unity 6C#Behaviour TreesAI AgentPerceptionMemoryState-based AIWeather SystemNight CycleHelicopter RescueStandalone WindowsAcademic Project
Unity 6Behaviour TreesScout AIWolf · LionNight CycleWindows Build
LostBoyScoutAdventure.exe · Unity 6
LostBoyScoutAdventure.exe · Unity 6
LostBoyScoutAdventure.exe · Unity 6
LostBoyScoutAdventure.exe · Unity 6
Module
CS6P05ES Artificial Intelligence
Engine
Unity 6000.3.10f1
Language
C#
Original Unity gameplay embeds extracted from the CS6P05ES report / user manual (pdfimages) - not screenshots of PDF pages.
Engine
Unity 6
Editor 6000.3.10f1
Agent focus
Scout AI
Perception · BT · Memory
Predators
2
Wolf (L1) · Wolf+Lion (L2)
Outcomes
Multi
Exit · Rescue · Multiple lose paths
Overview
Lost Boy Scout Adventure (coursework title also uses Scout vs Wolf: Intelligent Agent Survival Game) is a Unity survival simulation focused on autonomous AI behaviour. The scout senses the environment, updates survival needs, and chooses actions based on current priorities.
Problem statement
Intelligent-agent coursework needs behaviour that can be observed, not a fixed animation path. The scout must sense threats and resources, manage competing survival needs, and produce different endings across runs under uncertainty.
- Demonstrate perception, decision making, searching, memory, and status-based behaviour in Unity.
- Keep sensing partial (vision/hearing) so the agent is not omniscient.
- Make state changes visible for assessment through HUD labels, overlays, and alternative outcomes.
Trust boundaries
- Academic Unity artefact for CS6P05ES - not a commercial game release.
- Canonical repo is LostBoyScoutAdventures; LostBoyScoutAdventure is an earlier prototype.
- No weapons/inventory RPG systems are claimed - survival stats, pickups, predators, exit/rescue.
- No gameplay video is bundled in the public repositories.
How a run unfolds
Observable survival loop from the report's state labels - priorities shift as threats and needs change.
01
Explore
Scout wanders the jungle under Explore while hunger, water, and energy decay.
02
Find resources
SearchFood / SearchWater via vision, short-range sense, and remembered pickup points.
03
Avoid predator
Wolf (L1) or wolf+lion (L2) detect, investigate, and chase - scout can EscapeDanger.
04
Shelter
Safe-zone shelter for recovery and night/rain pressure without unfair predator catches.
05
Escape / exit
GoToExit when stats and threat allow - exit trigger is a primary win path.
06
Helicopter rescue
After rescue unlock, enter the green rescue circle for an alternate win outcome.
AI features
Intelligence traits from the CS6P05ES report - behaviour tree, perception, memory, priorities, and environmental influence - shown with real evidence screenshots.
Perception
ScoutPerception limits knowledge to vision cones, hearing radius, and short-range resource sense - not full-map omniscience. Night and rain modify sensing.
Memory
ScoutMemory stores food/water/danger points and forgets consumed resources so the scout can return to remembered locations.
Wolf AI
WolfBrain patrols, investigates, and chases via vision/hearing - the scout can enter EscapeDanger when the wolf closes in.
Lion AI
Level 2 adds LionAI alongside the wolf for dual-predator pressure with independent roam/chase behaviour.
Shelter · Food · Water
Survival needs drive SearchFood / SearchWater / shelter priorities under partial observability.
Weather influence
Rain and related GameManager modifiers affect sensing and movement presentation during survival runs.
Night cycle
Night reduces vision and movement context - torch, shelter, and HUD Day/Night status make the modifier assessable.
Helicopter rescue
After the rescue timer unlocks, entering the green rescue circle is an alternate win path alongside the exit.
Objectives
- Implement an autonomous scout that updates behaviour during gameplay.
- Implement state-based responses to internal and external events.
- Implement perception via vision, hearing, and short-range resource detection.
- Implement priority-driven decision making and searching for food, water, shelter, and exit.
- Implement memory of resources/danger and support exit success, rescue success, and multiple lose conditions.
- Include physics indicators (ranges, decay, speed modifiers, collisions) and complete documentation/test evidence.
Gameplay
Unity 2D top-down intelligent-agent survival simulation
Story
A boy scout is lost in a jungle map and must survive by managing hunger, water, and energy while avoiding predators and seeking exit escape or helicopter rescue.
Controls
- Play (AI): scout decisions driven by behaviour-tree brain.
- Manual Play: WASD / arrow keys; AI brain skipped while manual.
- Pause: Esc / P. Main menu: Options, Help, Quit.
Characters
- Scout (Player tag) - primary intelligent agent
Enemies
- Wolf - Level 1
- Lion - Level 2 additional predator
Collectibles
- Food pickups
- Water pickups
Systems
- Shelter
- Exit
- Helicopter rescue circle
- Day/night + rain presentation
Development timeline
01
AI coursework brief
CS6P05ES Intelligent Agent artefact - demonstrate perception, decisions, and dynamic behaviour in Unity.
02
Scout survival concept
Hungry/thirsty scout in a changing jungle with predators, shelter, exit, and rescue outcomes.
03
Early prototype
LostBoyScoutAdventure snapshot with FSM-style scout/wolf scripts (not the finished artefact).
04
Adventures build
LostBoyScoutAdventures adds behaviour trees, perception, memory, lion, menus, and HUD overlays.
05
Level scenarios
Level 1 wolf scenario and Level 2 wolf+lion scenario for controlled predator evaluation.
06
Documentation package
Report, diagrams, screenshots, testing notes, and user manual for the approved individual submission.
Product deep dive
Main menu and modes
LOST BOY SCOUT - Adventure Survival opens with Play (AI), Manual Play, Options, Help, and Quit so assessors can choose autonomous or keyboard-driven evaluation.
Unity UIC#MainMenuPauseController
- Unity productName is LostBoyScoutAdventure; coursework report title uses Scout vs Wolf wording.
- Level selection supports wolf-only and wolf+lion predator scenarios.
- Debug weather/resource buttons aid demonstration without changing default survival rules.
Behaviour-tree scout AI
The scout chooses actions from runtime status and sensed events through priority behaviour-tree logic rather than a fixed scripted sequence.
Behaviour TreesScoutPerceptionScoutMemory
- ScoutBrain builds selector/sequence trees; ScoutAI mirrors readable StateLabel values.
- Perception uses vision cones and hearing with night/rain modifiers.
- Memory stores resource and danger points and supports behaviours such as returning to remembered food.
Predator scenarios
Level 1 focuses on the wolf; Level 2 adds a lion. Predators patrol, investigate, and chase, while the scout can enter flee behaviour when danger is detected.
WolfBrainLionAISteering
- Vision overlays and action labels make predator and scout states assessable on screen.
- Catch distance triggers lose outcomes such as killed by wolf/lion.
- Cheat aids (for demos) are not presented as default difficulty.
Win and lose outcomes
Success paths include reaching the exit with safe stats or entering the helicopter rescue circle after the rescue timer unlocks. Lose paths cover starvation, dehydration, exhaustion, and predator capture.
GameManagerHelicopterRescueViewHUD
- HUD tracks Day status plus Hunger, Water, and Energy bars.
- Helicopter pickup prompts instruct moving into the green rescue circle.
- Alternative endings keep runs replayable for intelligent-agent evaluation.
System design diagrams
Coursework diagrams cover scout state transitions, class structure, runtime game flow, and the P.E.A.S model used to frame the intelligent agent.
draw.ioUnity C#
- State transition diagram links survival modes and conceptual transitions.
- Class diagram highlights ScoutAI, ScoutBrain, predators, and GameManager relationships.
- P.E.A.S summarises performance measures, environment, actuators, and sensors.
System design
Canonical implementation lives in LostBoyScoutAdventures. ScoutAI facades a ScoutBrain behaviour tree with perception and memory; predators use WolfAI/WolfBrain and LionAI; GameManager owns world cycle, rescue timing, and win/lose; menus and HUD coordinate presentation.
Agents
ScoutAI / ScoutBrain, WolfAI / WolfBrain, LionAI, SteeringAgent.
Sensing & memory
ScoutPerception (vision/hearing) and ScoutMemory with decay and discovery feedback.
World
GameManager day/night, rain, helicopter chance, exit/rescue triggers.
Presentation
Main/pause menus, survival HUD, vision cones, action labels, debug panels.
Technology stack
Unity 6000.3.10f1 (Unity 6)C#Behaviour trees2D top-down scenesPerception (vision / hearing)Memory systemSteering / movement helpersCanvas HUD & menus
Challenges
Competing survival goals
Hunger, thirst, energy, predators, weather, shelter, exit, and rescue compete every tick - behaviour-tree priorities must stay readable and testable.
Partial observability
Vision/hearing limits and night/rain modifiers prevent omniscient pathing and force memory-backed searching.
Assessable presentation
State labels, vision cones, HUD, and menus must reveal AI reasoning without turning the artefact into a pure debug toy.
Solutions
- Facade agents (ScoutAI/WolfAI) over behaviour-tree brains with mirrored state labels for screenshots and viva explanation.
- Perception + memory modules instead of tag-omniscient FSM-only control in the finished Adventures build.
- Separate Level 1/Level 2 predator setups and AI vs Manual play modes for controlled evaluation.
Engineering decisions
- Unity 6 (6000.3.10f1) 2D top-down for visible agent behaviour.
- Behaviour trees for scout/wolf decision making in the canonical Adventures repo.
- Optional manual control sharing the same survival systems.
- Document physics indicators (ranges, decay, collisions) without claiming NavMesh A* (listed as future work).
Development process
- 01Define intelligent-agent requirements and P.E.A.S framing.
- 02Prototype survival stats and predator chase, then upgrade to BT/perception/memory.
- 03Build menus, HUD, Level 1/2 scenarios, and demo overlays.
- 04Produce diagrams, testing evidence, user manual, and coursework report package.
Testing
- Gameplay observation of AI mode, manual mode, predator responses, and win/lose paths.
- Debug/HUD overlays used as assessment aids for active nodes and discovery events.
- Coursework package includes diagrams, screenshots, and supporting manual evidence.
- No public gameplay video is committed in the GitHub repositories.
Lessons learned
- Visible state labels make behaviour-tree decisions explainable to assessors.
- Memory + partial sensing produces more convincing agent behaviour than omniscient searches.
- Demo cheats must stay clearly separated from default survival difficulty claims.
Future improvements
- A* pathfinding around obstacles.
- Richer adaptive memory scoring or reinforcement learning extensions.
- Improved character animation and movement presentation.
- More advanced predator tactics and cooperation.
Media gallery
Steam-style media set from the coursework PDF and Unity captures - filter by category; lightbox with arrows, Esc, and zoom.
Downloads
Real artefacts only - Windows build (Appendix E Drive link), manuals, technical report, and GitHub source.
Windows BuildZIP · WindowsDirect download of the Windows standalone build. Extract and run LostBoyScoutAdventure.exe beside UnityPlayer.dll and the data folder.Windows Build (Drive fallback)FallbackUse only if the primary download fails - same Appendix E package via Google Drive direct download.Source CodeGitHubCanonical Unity project: LostBoyScoutAdventures.User ManualPDFGame User Manual (Appendix F) - launch, controls, AI/manual modes, HUD, outcomes.Technical ReportPDF · 5.7 MBFull CS6P05ES coursework assessment PDF.Design DiagramsPDFState transition, class diagram, flowchart, P.E.A.S extracts.Build EvidencePDFAppendix E page documenting the Windows build folder and Drive link.
After downloading the Windows package, keep LostBoyScoutAdventure.exe, UnityPlayer.dll, and the data folder together.
Conclusion
The artefact demonstrates perception, decision making, searching, memory, and status-based behaviour in a replayable Unity survival setting with alternative win and lose outcomes - a practical intelligent-agent submission backed by diagrams, screenshots, a Windows build, and the CS6P05ES report.