The document discusses the AI systems used in the game Left 4 Dead. It describes how the game involves cooperative survival against hordes of infected zombies. It outlines goals for the AI, including delivering robust zombie behaviors, providing competent AI-controlled survivors, promoting replayability through procedural generation, and generating dramatic pacing. It then goes into details about the various systems used to achieve these goals, such as behavior trees, pathfinding, animation, event handling, and systems for procedural enemy placement.
Scalable systems make us face challenges like concurrency, distribution, fault tolerance, elasticity, etc. Akka not only steps up to meet these, but it makes writing scalable software particularly easy. We will demo Akka's most important tool, the actor model, using a vivid example and live coding.
Player Traversal Mechanics in the Vast World of Horizon Zero DawnGuerrilla
Download the original PowerPoint presentation here: http://www.guerrilla-games.com/read/player-traversal-mechanics-in-the-vast-world-of-horizon-zero-dawn
Paul van Grinsven shows what is needed to make Aloy traverse the vast world of Horizon Zero Dawn, with its complex and organic environments. Various traversal mechanics are covered from a gameplay programmer's perspective, focusing on the interaction between code and animations. The different systems and techniques involved in the implementation of these mechanics are explained, and Van Grinsven looks at the underlying reasoning and design decisions.
Brains@Bay Meetup: The Increasing Role of Sensorimotor Experience in Artifici...Numenta
We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. The first step was to view AI as the design of agents which interact with the world and thereby have sensorimotor experience; this viewpoint became prominent in the 1980s and 1990s. The second step was to view the goal of intelligence in terms of experience, as in the reward signal of optimal control and reinforcement learning. The reward formulation of goals is now widely used but rarely loved. Many would prefer to express goals in non-experiential terms, such as reaching a destination or benefiting humanity, but settle for reward because, as an experiential signal, reward is directly available to the agent without human assistance or interpretation. This is the pattern that we see in all four steps. Initially a non-experiential approach seems more intuitive, is preferred and tried, but ultimately proves a limitation on scaling; the experiential approach is more suited to learning and scaling with computational resources. The third step in the increasing role of experience in AI concerns the agent’s representation of the world’s state. Classically, the state of the world is represented in objective terms external to the agent, such as “the grass is wet” and “the car is ten meters in front of me”, or with probability distributions over world states such as in POMDPs and other Bayesian approaches. Alternatively, the state of the world can be represented experientially in terms of summaries of past experience (e.g., the last four Atari video frames input to DQN) or predictions of future experience (e.g., successor representations). The fourth step is potentially the biggest: world knowledge. Classically, world knowledge has always been expressed in terms far from experience, and this has limited its ability to be learned and maintained. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.
Презентация хакатона Apps4All 13-14 декабря 2013Apps4All
ХАКАТОН APPS4ALL: прими участие в ключевом событии хакатон-сцены
Участие в ХАКАТОНЕ абсолютно бесплатное – достаточно зарегистрироваться на сайте мероприятия.
В данный момент партнеры хакатона Google, Intel, Microsoft представили 3 номинации, но в скором времени их должно стать еще больше!
Приглашаем всех на наш предновогодний ХАКАТОН APPS4ALL!
http://hackathon.apps4all.ru/
Презентация хакатона Apps4All 13-14 декабря 2013Apps4All
ХАКАТОН APPS4ALL: прими участие в ключевом событии хакатон-сцены
Участие в ХАКАТОНЕ абсолютно бесплатное – достаточно зарегистрироваться на сайте мероприятия.
В данный момент партнеры хакатона Google, Intel, Microsoft представили 3 номинации, но в скором времени их должно стать еще больше!
Приглашаем всех на наш предновогодний ХАКАТОН APPS4ALL!
http://hackathon.apps4all.ru/
Scalable systems make us face challenges like concurrency, distribution, fault tolerance, elasticity, etc. Akka not only steps up to meet these, but it makes writing scalable software particularly easy. We will demo Akka's most important tool, the actor model, using a vivid example and live coding.
Player Traversal Mechanics in the Vast World of Horizon Zero DawnGuerrilla
Download the original PowerPoint presentation here: http://www.guerrilla-games.com/read/player-traversal-mechanics-in-the-vast-world-of-horizon-zero-dawn
Paul van Grinsven shows what is needed to make Aloy traverse the vast world of Horizon Zero Dawn, with its complex and organic environments. Various traversal mechanics are covered from a gameplay programmer's perspective, focusing on the interaction between code and animations. The different systems and techniques involved in the implementation of these mechanics are explained, and Van Grinsven looks at the underlying reasoning and design decisions.
Brains@Bay Meetup: The Increasing Role of Sensorimotor Experience in Artifici...Numenta
We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. The first step was to view AI as the design of agents which interact with the world and thereby have sensorimotor experience; this viewpoint became prominent in the 1980s and 1990s. The second step was to view the goal of intelligence in terms of experience, as in the reward signal of optimal control and reinforcement learning. The reward formulation of goals is now widely used but rarely loved. Many would prefer to express goals in non-experiential terms, such as reaching a destination or benefiting humanity, but settle for reward because, as an experiential signal, reward is directly available to the agent without human assistance or interpretation. This is the pattern that we see in all four steps. Initially a non-experiential approach seems more intuitive, is preferred and tried, but ultimately proves a limitation on scaling; the experiential approach is more suited to learning and scaling with computational resources. The third step in the increasing role of experience in AI concerns the agent’s representation of the world’s state. Classically, the state of the world is represented in objective terms external to the agent, such as “the grass is wet” and “the car is ten meters in front of me”, or with probability distributions over world states such as in POMDPs and other Bayesian approaches. Alternatively, the state of the world can be represented experientially in terms of summaries of past experience (e.g., the last four Atari video frames input to DQN) or predictions of future experience (e.g., successor representations). The fourth step is potentially the biggest: world knowledge. Classically, world knowledge has always been expressed in terms far from experience, and this has limited its ability to be learned and maintained. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.
Презентация хакатона Apps4All 13-14 декабря 2013Apps4All
ХАКАТОН APPS4ALL: прими участие в ключевом событии хакатон-сцены
Участие в ХАКАТОНЕ абсолютно бесплатное – достаточно зарегистрироваться на сайте мероприятия.
В данный момент партнеры хакатона Google, Intel, Microsoft представили 3 номинации, но в скором времени их должно стать еще больше!
Приглашаем всех на наш предновогодний ХАКАТОН APPS4ALL!
http://hackathon.apps4all.ru/
Презентация хакатона Apps4All 13-14 декабря 2013Apps4All
ХАКАТОН APPS4ALL: прими участие в ключевом событии хакатон-сцены
Участие в ХАКАТОНЕ абсолютно бесплатное – достаточно зарегистрироваться на сайте мероприятия.
В данный момент партнеры хакатона Google, Intel, Microsoft представили 3 номинации, но в скором времени их должно стать еще больше!
Приглашаем всех на наш предновогодний ХАКАТОН APPS4ALL!
http://hackathon.apps4all.ru/
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. What is Left 4 Dead?
Left 4 Dead is a
replayable, cooperative,
survival-horror game
where four Survivors
cooperate to escape
environments swarming
with murderously enraged
“Infected” (ie: zombies)
10. Goals of Left 4 Dead AI
Deliver Robust Behavior Performances
Provide Competent Human Player Proxies
Promote Replayability
Generate Dramatic Game Pacing
11. Goals of Left 4 Dead AI
Deliver Robust Behavior Performances
Provide Competent Human Player Proxies
Promote Replayability
Generate Dramatic Game Pacing
12. Goal: Deliver Robust Behavior Performances
Moving through the Environment
• A* through Navigation Mesh
• Creates jagged path
• How to move fluidly?
13. Goal: Deliver Robust Behavior Performances
Path Optimization
• Collapse redundant path
nodes
• Good
• Creates minimal and direct
path
• Bad
• Nontrivial CPU cost at path
build time
• Paths are recomputed
often in the game
• Following path directly
looks robotic
14. Goal: Deliver Robust Behavior Performances
Reactive Path Following
• Move towards “look ahead”
point farther down path
• Use local obstacle avoidance
• Good
• (Re)pathing is cheap
• Local avoidance handles small
physics props, other bots,
corners, etc
• Superposes well with mob
flocking behavior
• Resultant motion is fluid
• Bad
• Can avoid off path too much,
requiring repath
28. Goal: Deliver Robust Behavior Performances
Path Optimization vs
Reactive Path Following
• Resultant path is reasonably
close to optimized path
• Reactive Path Following is
used for all actors in Left 4
Dead
29. Goal: Deliver Robust Behavior Performances
Reactive Path Following: Climbing
• To ensure the Common Infected horde are always
dangerous, they have the ability to rapidly climb
• Climbing is algorithmic, using a similar technique to local
obstacle avoidance
• Algorithmic climbing solves two major issues
• Navigating the complex geometry of the post apocalyptic world
of Left 4 Dead
• Navigating over movable physics props
31. Goal: Deliver Robust Behavior Performances
Approach
• Bot periodically tests for
obstacles immediately
ahead as it follows its path
32. Goal: Deliver Robust Behavior Performances
Find Ceiling
• Once an obstacle has been
detected, another hull trace
determines the available
vertical space above the bot
33. Goal: Deliver Robust Behavior Performances
Find Ledge
• The vertical space is then
scanned via a series of hull
traces from lowest to
highest to find the first
unobstructed hull trace
34. Goal: Deliver Robust Behavior Performances
Find Ledge Height
• Another hull trace
downward from the
unobstructed trace finds the
precise height of the ledge
to be climbed
35. Goal: Deliver Robust Behavior Performances
Find Ledge Forward Edge
• A final series of downward
hull traces that step
backwards determine the
forward edge of the ledge
36. Goal: Deliver Robust Behavior Performances
Perform Climb
• Pick closest match
animation from dozens of
motion captured climb G rr. A rg h .
animations of various height
increments
38. Goal: Deliver Robust Behavior Performances
Locomotion A c to r
Owns moving the actor to a new
position in the environment (collision L o c o m o ti o n
resolution, etc)
Body B ody
Owns animation state
Vision
Owns line-of-sight, field of view, and
V i si o n
“am I able to see <X>” queries.
Maintains recognized set of entities. In te n ti o n
Intention
Contains and manages a system of B e h a vi o r B e h a vi o r
concurrent Behaviors (HFSM+Stack) A c ti o n A c ti o n
• Behavior
Contains and manages a system of A ct i on A ct i on
Actions A ct i on A ct i on
• Action
The building blocks of intentional
actions. An Action can contain a child
Action, which executes concurrently.
39. Goal: Deliver Robust Behavior Performances
Encapsulation of Action processing
• OnStart
• Executed when the Action is transtioned into
• Can return an immediate transition
• Update
• Does the “work” of the Action
• Update can return a transition to a new Action
• OnEnd
• Is executed when the Action is transitioned out of
• OnSuspend
• Executed when Action has been put on hold for another Action
• Can return a transition
• OnResume
• Executed when Action resumes after being suspended
• Can return a transition (perhaps Action is no longer valid)
40. Goal: Deliver Robust Behavior Performances
Action Transitions
• Continue()
No transition, continue this Action next frame.
• ChangeTo( NextAction, “reason” )
Exit the current Action and transition into NextAction.
• SuspendFor( NextAction, “reason” )
Put the current Action “on hold” (bury it) and enter NextAction.
• Done( “reason” )
This Action is finished. Resume the “buried” Action.
• “reason”
A string documenting the reason why the transition occurred (ie:
“The player I was chasing died”). Reason strings help clarify the
Action system and are invaluable for realtime debug output.
41. Goal: Deliver Robust Behavior Performances
Encapsulation of Action Transitions
• Returning ActionResults enforces atomic transitions since no
behavior code can be run between the decision to change to a new
Action and the actual change
• The only way to go from Action A to Action B is for Action A to
return a transition to Action B
• There is no way to force a transition from Action A to Action B
without Action A’s “knowledge”
• Keeps related behavior code encapsulated within the Action
42. Goal: Deliver Robust Behavior Performances
Event Propagation E v e n t: O n In ju re d
Events propagate to all A c to r
components L o c o m o ti o n
In the Intention subsystem,
events are B ody
• First handled by the V i si o n
innermost child Action
• If child has no response, In te n ti o n
event is sent to buried B e h a vi o r B e h a vi o r
Action A c ti o n A c ti o n
• If no buried Actions have a A ct i on A ct i on
response, event is sent to
A ct i on A ct i on
parent
43. Goal: Deliver Robust Behavior Performances
Example events
• OnMoveToSuccess, OnMoveToFailure,
OnStuck, OnUnStuck,
OnAnimationActivityComplete,
OnAnimationEvent,
OnContact, OnInjured,
OnKilled, OnOtherKilled,
OnSight, OnLostSight,
OnSound,
OnPickUp, OnDrop
Actions can implement event handler
methods, providing context-specific
reactions to game events
44. Goal: Deliver Robust Behavior Performances
Contextual Query System
Actions can implement query methods, providing context-
specific answers to questions
Defines queries that can be asked of the Behavior/Action system
as a “black box”
Queries propagate from childmost Action outward until an
answer is given, identically to Event propagation
Allows concurrent Behaviors to coordinate
• ie: We are able to opportunistically able to pick up an object,
should we?
45. Goal: Deliver Robust Behavior Performances
Example Queries
• SelectMoreDangerousThreat( me, subject, threat1, threat2 )
• Allows bot to find the most dangerous threat to someone else
(ie: an incapacitated player it wants to help)
• ShouldPickUp
• ShouldHurry
• IsHindrance
46. Goals of Left 4 Dead AI
Deliver Robust Behavior Performances
Provide Competent Human Player Proxies
Promote Replayability
Generate Dramatic Game Pacing
47. Goal: Provide Competent Human Player Proxies
Survivor Bots
• Allowed us to assume 4 player Survivor team for game tuning and balance
• Drop in/out (“Take a Break”) incredibly valuable in the wild
• Automated stress testing with 4 SurvivorBots and accelerated game time
48. Goal: Provide Competent Human Player Proxies
Believability/Fairness
• Players need to believe bot
replacements are “fair”
• Imperfect knowledge –
simulated senses
• Simulated aiming
• Reaction times
• Reliable and predictable
decision making
• Difficult issue: Rescuing
incapacitated humans
49. Goal: Provide Competent Human Player Proxies
Trust
• Cooperative nature of game
requires close collaboration of
Survivor team and implicit trust
in teammate’s action choices
• SurvivorBots prioritize human
teammates over bots
• Game “cheats” to guarantee
some undesirable events
cannot occur
• SurvivorBots cannot deal
friendly fire damage
• SurvivorBots never use
Molotovs
• If a SurvivorBot ever gets far
out of place for any reason, it
is teleported near the team
when no human is looking at it
50. Goal: Provide Competent Human Player Proxies
SurvivorBots exercised complexity encapsulation
• Two concurrent Behavior systems: Main and Legs
• Main: Primary decision making, attention, target
selection and attack
• Legs: Slaved to Main, responsible for staying near
team unless otherwise directed
• Many hierarchical behavior states reacting to
dozens of game events
• Interrupting, resuming
• Complex contextual decision making
• Tank, Witch, Boomer, Smoker, Hunter, Mob rushes,
wandering horde, Survivor teammates, item
scavenging, healing self, healing teammates,
weapon selection, Rescue Closets, incapacitation,
limping, complex 3D terrain, movable physics
obstacles, transient fires, ladders, elevators, in any
combination
• Replicating human performance based on actual
gameplay experience
• Behavior system built to reproduce decisions and
actions players make while playing the game
51. Goals of Left 4 Dead AI
Deliver Robust Behavior Performances
Provide Competent Human Player Proxies
Promote Replayability
Generate Dramatic Game Pacing
52. Goal: Promote Replayability
Replayability promotes long-term engagement with the game,
resulting in growth of the game’s community
• Growing online community results in ongoing sales
• Creates exposure opportunities for other related content
Examples: Counter-Strike, Team Fortress, Day of Defeat
• Thriving online communities with ongoing sales revenue
• Comparatively few maps, played repeatedly for years on end
• Unpredictable experience created by interactions between teams of
players drives replayability
53. Goal: Promote Replayability
In Left 4 Dead, Procedural Population of enemies
and loot strongly promotes replayability
• Creates unpredictable encounters analogous to CS, TF, DoD
• Game session is viewed as a skill challenge instead of a
memorization exercise
54. Goal: Promote Replayability
Static placement of enemies and loot hinders
replayability
• Players are good at memorizing all static locations
• Removes suspense of not knowing what will happen next
• Results in “optimal strategy” that works every time
• Players expect everyone to have memorized all encounters
• Kills cooperation and degenerates into a race
Even multiple sets of manually placed triggers/scripts
fails
• Players learn all of them, and if they don’t see A, they
prepare for B or C, etc.
55. Goal: Promote Replayability
Procedurally Populated Environments
How do we procedurally populate the environment
with interesting distributions of enemies?
• Using Structured Unpredictability
• Part of the collection of systems called the “AI Director”
56. Goal: Promote Replayability
Procedurally Populated Environments
First, a short summary of tools used to generate
Structured Unpredictability
• Navigation Mesh
• Flow Distance
• Potential Visibility
• Active Area Set
57. Goal: Promote Replayability
Procedurally Populated Environments
The Navigation Mesh
• Represents “walkable space”
• Originally created for Counter-
Strike Bot pathfinding
• Useful for general spatial
reasoning and spatially
localized information
• Has area A ever been seen by
actor B?
• Is area X potentially visible by
area Y?
• Where is a spot near the
Survivors, not visible to any of
them?
• How far have we traveled to
reach this area?
58. Goal: Promote Replayability
Procedurally Populated Environments
“Flow Distance”
• Travel distance from the
starting safe room to each
area in the navigation mesh
• Following increasing flow
gradient always takes you to
the exit room
• “Escape Route” = shortest
path from start safe room to
exit
• Used as a metric for
populating enemies and loot,
and for answering questions
such as “is this spot ahead or
behind the Survivor group”
59. Goal: Promote Replayability
Procedurally Populated Environments
The Active Area Set (AAS)
E X IT
• The set of Navigation Areas
surrounding the Survivor team
• The AI Director creates/destroys
population as the AAS moves
through the environment
• Allows for hundreds of enemies
using a small set of reused entities
E NT E R
60. Goal: Promote Replayability
Procedurally Populated Environments
Potentially Visible Areas
E X IT
• The set of Navigation Areas
potentially visible to any Survivor
E NT E R
64. Goal: Promote Replayability
Procedurally Populated Environments
Populating via Structured Unpredictability
• Not purely random, nor deterministically uniform
• Population functions where space and/or time varies based
on designer-defined amount of randomization
• Example: Mob spawns
• Occur at randomized interval between 90 and 180 seconds
• Located at randomized spot “behind” the Survivor team
• Structured Unpredictability = Superposition of several of
these population functions
65. Goal: Promote Replayability
Procedurally Populated Environments
Structured Unpredictability in Left 4 Dead
• Wanderers (high frequency)
• Common Infected that wander around in a daze until alerted by a
Survivor
• Mobs (medium frequency)
• A large group (20-30) of enraged Common Infected that periodically
rush the Survivors
• Special Infected (medium frequency)
• Individual Infected with special abilities that harass the Survivor team
• Bosses (low frequency)
• Powerful Infected that force the Survivors to change their strategy
• Weapon Caches (low frequency)
• Collections of more powerful weapons
• Scavenge Items (medium frequency)
• Pipe bombs, Molotovs, Pain Pills, Extra Pistols
66. Goal: Promote Replayability
Procedurally Populated Environments
Populating Wandering Infected
• Stored as a simple count (N) in each area
• Counts are randomly determined at map
(re)start based on Escape Route length
and desired density
• When an area enters the AAS
• Create N Infected (if possible)
• When an area leaves the AAS, or a
pending Mob needs more members
• Wanderers in the area are deleted and N is
increased accordingly
• Wanderer count (N) is zeroed:
• When an area becomes visible to any
Survivor
• When the Director is in Relax mode
67. Goal: Promote Replayability
Procedurally Populated Environments
Example E X IT
• Green areas are entering
the AAS and wanderers are
being spawned
• Red areas are leaving the
AAS, and the Infected
within will be de-spawned
E NT E R
68. Goal: Promote Replayability
Procedurally Populated Environments
Populating Mobs
• Created at randomized
intervals (90-180 seconds on
Normal difficulty)
• Boomer Vomit forces Mob
spawn, resets random
interval
• Mob size grows from
minimum just after spawn to
maximum after a duration to
balance difficulty of
successive, frequent Mobs
69. Goal: Promote Replayability
Procedurally Populated Environments
Populating the Special Infected
• Created at individually randomized intervals
• Use valid area in the AAS not visible by the Survivor team
appropriate to Special’s class
• Boomers spawn ahead, since they are slow and can’t chase well
• Smokers attempt to select areas above the Survivor team
70. Goal: Promote Replayability
Procedurally Populated Environments
Where to spawn
E X IT
• Behind Survivors
• Only select valid areas in the AAS
that are at or behind the Survivor
team’s “flow” distance
• 75% of Mobs come from behind,
since wanderers and
Special/Boss Infected are usually
engaged ahead of the team
E NT E R
71. Goal: Promote Replayability
Procedurally Populated Environments
Where to spawn
E X IT
• Ahead of Survivors
• Only select valid areas in the AAS
that are at or greater than the
Survivor team’s “flow” distance
E NT E R
72. Goal: Promote Replayability
Procedurally Populated Environments
Where to spawn
E X IT
• Near Boomer Vomit Victim
• Only select valid areas in the AAS
that are near the Boomer Vomit
Victim’s “flow” distance
E NT E R
73. Goal: Promote Replayability
Procedurally Populated Environments
Where to spawn
E X IT
• Anywhere
• Any valid area in the AAS
• Default if there are no valid
areas in the more specific sets
E NT E R
74. Goal: Promote Replayability
Procedurally Populated Environments
Boss Population
• Positioned every N units along
“escape route” +/- random
amount at map (re)start.
• Three Boss events are shuffled
and dealt out: Tank, Witch, and
Nothing.
• Successive repeats are not
allowed (ie: Tank, then Tank
again)
Tank Witch Witch Tank Witch Tank
75. Goal: Promote Replayability
Procedurally Populated Environments
Weapon Caches
• Map designer creates several possible weapon
caches in each map, the AI Director chooses
which will actually exist
Scavenge Items
• Map designer creates many possible item groups
throughout the map, the AI Direction chooses
which groups actually exist
Why designer-placed?
• Prediction of possible locations beneficial in this
case
• Allows visual storytelling/intention
• Solves item placement issues (leaning against
wall, mounted in gun rack, etc)
76. Goal: Promote Replayability
Procedural Content
• Promotes replayability
• Solution for linear, yet replayable multiplayer experiences
• Greatly multiplies output of development team
• Improves community created content
77. Goals of Left 4 Dead AI
Deliver Robust Behavior Performances
Provide Competent Human Player Proxies
Promote Replayability
Generate Dramatic Game Pacing
78. Goal: Generate dramatic game pacing
What do we mean by “dramatic game pacing”?
• Algorithmically adjusting game pacing on the fly to maximize
“drama” (player excitement/intensity)
Inspired by Observations from Counter-Strike
• Natural pacing of CS is "spiky”, with periods of quiet tension
punctuated by unpredictable moments of intense combat
• Constant, unchanging combat is fatiguing
• Long periods of inactivity are boring
• Unpredictable peaks and valleys of intensity create a powerfully
compelling and replayable experience
• Same scenario, often the same map, yet different and compelling
experience each round
79. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing algorithm
• Creates peaks and valleys of intensity similar to the proven
pacing success of Counter-Strike
• Algorithm:
• Estimate the “emotional intensity” of each Survivor
• Track the max intensity of all 4 Survivors
• If intensity is too high, remove major threats for awhile
• Otherwise, create an interesting population of threats
• Another key system of the “AI Director”
80. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
Estimating the “emotional intensity” of each
Survivor
• Represent Survivor Intensity as a value
• Increase Survivor Intensity
• When injured by the Infected, proportional to
damage taken
• When the player becomes incapacitated
• When player is pulled/pushed off of a ledge
by the Infected
• When nearby Infected dies, inversely
proportional to distance
• Decay Survivor Intensity towards zero over
time
• Do NOT decay Survivor Intensity if there are
Infected actively engaging the Survivor
81. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
Use Survivor Intensity to modulate the Infected population
• Build Up
• Create full threat population until Survivor Intensity crosses peak
threshold
• Sustain Peak
• Continue full threat population for 3-5 seconds after Survivor
Intensity has peaked
• Ensures minimum “build up” duration.
• Peak Fade
• Switch to minimal threat population (“Relax period”) and monitor
Survivor Intensity until it decays out of peak range B uild
• This state is needed so current combat engagement can play out Up
without using up entire Relax period. Peak Fade won’t allow the
Relax period to start until a natural break in the action occurs.
• Relax R e lax
S us tain
• Maintain minimal threat population for 30-45 seconds, or until P e ak
Survivors have traveled far enough toward the next safe room,
then resume Build Up.
P e ak
F ad e
82. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
Full Threat Population (Build Up)
• Wanderers
• Mobs
• Special Infected
Minimal Threat Population (Relax)
• No Wanderers until team is “calm”
• No Mobs
• No Special Infected (although existing Specials may attack)
Boss Encounters NOT affected by adaptive pacing
• Overall pacing affected too much if they are missing
• Boss encounters are intended to change up the pacing anyhow
83. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
An example procedurally generated population
84. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
85. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
86. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
87. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
88. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
89. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
90. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
How the AI Director modulates the population based
on the Survivor team’s “emotional intensity”
91. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing
Comparing population after modulation by the AI
Director
92. Goal: Generate dramatic game pacing
Adaptive Dramatic Pacing reacts to Survivor team
• Generates reliable peaks of intensity without completely
overwhelming the team
• Because of player variation and procedural threat population,
timing and location of peaks will differ
each time the game is played
Algorithm adjusts pacing, not difficulty
• Amplitude (difficulty) is not changed, frequency (pacing) is
Simple algorithms can generate compelling pacing schedules
• Survivor Intensity estimation is crude, yet the resulting pacing
works
93. Future Work
Just the beginning…
• Expand repertoire of verbs
available to the AI Director
• Explore further procedural
content generation
opportunities
• How to utilize these systems in
other game projects?
94. For more information…
www.L4D.com
www.valvesoftware.com
mike.booth at valvesoftware dot com