=Paper=
{{Paper
|id=Vol-2648/paper10
|storemode=property
|title=Automatic Hierarchical Task Network Planning System for the Unity Engine
|pdfUrl=https://ceur-ws.org/Vol-2648/paper10.pdf
|volume=Vol-2648
|authors=Boris Krylov,Maxim Abramov
}}
==Automatic Hierarchical Task Network Planning System for the Unity Engine==
https://ceur-ws.org/Vol-2648/paper10.pdf
Automatic hierarchical task network planning
system for the Unity game engine
Boris Krylova , Maxim Abramova,b
a
St. Petersburg State University, St. Petersburg, Russia
b
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia
Abstract
The article relates to the automatic planning system integrated into the Unity game engine environment,
which controls an agent behavior. A graphical notation for domain knowledge definition was developed.
Goals reached in this paper will allow quick complex behaviour pattern implementation for non-player
characters within the game environment.
1. Introduction
Social engineering attacks are different sets of applied psychological and analytical methods
and techniques used by malefactors to trick the users of public or corporate network into vi-
olating established rules and information security policies, aiming to steal data, personal in-
formation, money and even identity [19, 2]. These methods vary from simple use of victim’s
carelessness to sophisticated fraudulent schemes. In the most cases, it’s easier and cheaper for
scammers to get what they want from the users themselves, than try to find vulnerabilities in
the system [19].
The problem of social engineering attacks has been relevant for a long time. This fact is
confirmed by both the statistics of news agencies [7] and the research of well-known cyberse-
curity companies [20]. Based on the laboratory of theoretical and interdisciplinary problems
of computer science studies on the topic of automated assessing the level of user protection
via artificial intelligence methods such as Bayes networks [9, 10] and how to increase it are
being conducted [1]. Particularly, the level of protection from social engineering attacks of
information systems users can be increased through a digital educational game. Despite the
relevance of the task, confirmed by the demand for training, surveys, etc. from organizations,
such games have not yet been presented. The general direction of the research branch is to
solve the designated problem — the development of an educational game that helps to increase
user awareness of social engineering attacks and, consequently, security. As a part of this de-
velopment, it was necessary to propose a game artificial intelligence system, i.e. a component
of the game that serves to control non-player characters (NPC), creating the illusion of their
Russian Advances in Artificial Intelligence: selected contributions to the Russian Conference on Artificial intelligence
(RCAI 2020), October 10-16, 2020, Moscow, Russia
" krylovbs2301@gmai.com (B. Krylov); mva@dscs.pro (M. Abramov)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
meaningful activities. NPC, in turn, act as adversaries for the player and the crowd [4, 12] in
the game environment.
Typically, the game artificial intelligence system is used to solve following problems:
• Movement and pathfinding. This problem concerns the character moves within the game
environment taking into account surroundings, obstacles and environmental features.
• Interaction with the environment and simple behavioural reactions. To simulate differ-
ent activities NPC need to be able to interact with their surroundings and change their
behaviour according the changes of the environment.
• Decision-making. In other words, the choice between several different sequences of el-
ementary actions, the NPC can take to achieve the given task[4].
The work proposes an automatic hierarchical task network planning system, implemented as
a part of the development of educational game about social engineering attacks. This planning
system is out to generate sequences of NPC’s actions based on the current state of environ-
ment. This will solve the problem of simulating the decision-making process and behavioural
reactions. Creating instruments to encode and execute different planning problems allows to
set the multitude of behaviour patterns for non-player characters.
The main reasoning for choosing hierarchical task network planning is its expressiveness.
Some approaches to behaviour modeling became classic in video game industry, such as finite-
state machines (FSM) or behaviour trees[5, 4]. However, FSM approach limits the number
of different situations the system can process and the transition conditions [4]. The more
nuanced and complex behaviour it is required to model, the more compound and extensive
state diagram of the system becomes. On the other hand, automated planners are better suited
for the problems with multiple goals.
Making a believable simulation of human behaviour, composed of purposeful activities and
lots of varying reactions to the external stimuli, is in fact a problem with multiple goals. There-
fore, it is appropriate to use automated planning. There are two types of automated planners
that have been used in games: Stanford Institute Research Problem Solver (STRIPS) and Hier-
archial Task Network planning (HTN planning). But HTN is more expressive than STRIPS [6]
and allows more intuitive and easier approach to designing NPCs’ behaviour.
2. Related works
The development of game artificial intelligence has been repeatedly described in the scientific
literature [4, 5, 25]. In the book "Artificial Intelligence and Games" it is proposed to use planners
as the basis for such a system[25].
The problem of using automated planners to simulate meaningful activity in games has been
around . The most striking example of the use of symbolic planning in games is the F.E.A.R.
first-person horror shooter 2005 year. STRIPS planning (Stanford Institute Research Problem
Solver) was used in this game [18, 25]. The game gained fame thanks to its advanced system
of game AI, which simulated the behavior of a detachment of opponents through a complex
consisting of goals and actions needed to achieve said goals [17].
The faster and more efficient planning method is HTN planning [5]. The main idea of this
method is that the target task is decomposed into a network of subtasks, which in turn can be
divided into other networks of subtasks, and so on, until a network of tasks consists of elemen-
tary actions is obtained [6]. This approach allows to interpret and construct tasks according to
the “top-down” principle, which describes the domain knowledge using the cognitive approach
to the problem of categorizing tasks included in it [11].
In an academic environment, HTN planning has been used and studied for a very long time.
One of the first HTN planning systems, Nonlin, was developed and introduced in 1976 [22]. In
Nonlin, the task network was represented as a node graph. There was no obvious distinction
between composite and primitive tasks. Nonlin supported both ordered plans and partially
ordered plans.
Since then, many HTN planning systems have been developed, including SHOP2 (Simple
Hierarchial Ordered Planner 2)[14], which has been successfully used in crisis management
and logistics [6], production planning [6], projects [13, 6] and videogames [8]. For example, in
the game “The Elder Scrolls IV: Oblivion” a version of the SHOP2 system developed using Java
programming language.
Also, HTN planning was successfully applied in games such as Transformers: Fall of Cy-
bertron [5] and Horizon Zero Dawn [23], for which their own planning systems were devel-
oped. In academic environment, research on HTN planning in games is still relevant. For
example, in [16], a high-performance gaming artificial intelligence system based on HTN plan-
ning was proposed. The researchers were faced with the task of making a search for a suitable
plan as quick as possible. To do this, the size of the space in which the plan is searched was
minimally reduced.
In the paper [21] an automatic planning system for a real-time strategy was proposed. Since
real-time strategies have a very large space for a plan search, the researchers were faced the
task of creating an effective algorithm for searching in it.
Thus, it can be concluded that HTN planning systems have been successfully used to develop
games of various genres. Nevertheless, in the context of serious games about social engineering
attacks, the decision is being applied for the first time.
That is partly due to the fact that there are no serious video games dedicated to teaching
its users about social engineering attacks. The main pupose of the serious game presented by
K. Beckers and S. Pape is created for elicitation of the company’s vulnerabilities to the social
engineering attacks [3]. And while it can be used to teach players, it is still a board game
with no digital content whatsoever. Another game, "Playing safe", that was developed by M.
Newbould and S. Furnell, is actually a video game and aims to raise awareness [15]. But it was
designed to not have any NPCs, so naturally, there was no need in game AI.
3. Defining HTN planning
Hierarchical task network planning is based on the idea that any task in a set of tasks can be
performed using a primitive action, or it can be decomposed into a sequence of tasks on which
some constraints are imposed.
The HTN planning language is used to describe hierarchical task network planning prob-
lems.
3.1. Planning language
A planning language is a tuple of mutually disjoint sets of characters: a set of predicate char-
acters 𝑃 and a set of terms 𝑇 . A set of terms consists of a finite set of constant characters
and an infinite set of variable characters. Let 𝑄 be a set of predicates. A predicate is closed
if its terms do not contain variable symbols. A state 𝑠 ∈ 2𝑄 is a set of closed predicates for
which the closed-world assumption is adopted, i.e. any predicate that is not included in the set
is considered false [6].
3.2. Tasks
There are two types of tasks in HTN planning: primitive and compound.
Primitive tasks are denoted by 𝑡𝑝 (𝜏 ), where 𝜏 is a term. Primitive task set is denoted by 𝑇𝑝 .
Each primitive task is represented by an operator.
An operator 𝑜 is a tuple (𝑡𝑝 (𝜏 ), 𝑓𝑝𝑟𝑒 (𝑜), 𝑓𝑒𝑓 𝑓 (𝑜)), where 𝑓𝑝𝑟𝑒 (𝑜), 𝑓𝑒𝑓 𝑓 (𝑜) ∈ 2𝑄 are the preconditions
and effects of the operator, respectively. Preconditions can be both positive, then they are
denoted by 𝑓𝑝𝑟𝑒 (𝑜)+ , and negative — 𝑓𝑝𝑟𝑒 (𝑜)− . An operator is called applicable to a state 𝑠 if
(𝑓𝑝𝑟𝑒 (𝑜)+ ⊆ 𝑠) ∧ (𝑓𝑝𝑟𝑒 (𝑜)− ∩ 𝑠) = ∅. The result of this application will be a new state 𝑠 ∗= (𝑠 ⧵
𝑓𝑒𝑓 𝑓 (𝑜)− ) ∪ 𝑓𝑒𝑓 𝑓 (𝑜)+ , where 𝑓𝑒𝑓 𝑓 (𝑜) are the negative effects of 𝑜, and 𝑓𝑒𝑓 𝑓 (𝑜)+ are positive.
A composite problem is denoted by 𝑡𝑐 (𝑚) ∈ 𝑇𝑐 , where 𝑚 is a decomposition method[6].
3.3. Task networks and decomposition
A decomposition method 𝑚 is a tuple (𝑡𝑐 (𝑚), 𝑓𝑝𝑟𝑒 (𝑜), 𝑡𝑛(𝑚)), where 𝑡𝑛(𝑚) denotes a task network,
and 𝑓𝑝𝑟𝑒 (𝑜) is preconditions for the decomposition method.
A task network is a pair (𝑇 , Ψ), where 𝑇 is a finite set of tasks, and Ψ is a set of constraints
imposed on this set.
In the developed automatic planning system, the set of tasks of any task network is strictly
partially ordered, which is the only restriction that is imposed on a task set by default.
A decomposition method with preconditions 𝑓𝑝𝑟𝑒 (𝑜) is applicable to state 𝑠 in the same way
as an operator with the same preconditions is applied to this state. The result of applying the
decomposition method is a task network.
Let’s define a decomposition. Let the state s be given and 𝑡𝑛𝑐 = (𝑇𝑐 , ≺𝑐 ) be a task network (≺𝑐
is a relation of strict partial order). The method 𝑚, applicable to state 𝑠, decomposes 𝑡𝑛𝑐 into a
network of tasks 𝑡𝑛𝑛 , by replacing task 𝑡, if and only if the following conditions are satisfied:
• 𝑡 = 𝑡𝑐 (𝑚);
• 𝑡𝑛𝑛 ∶= ((𝑇𝑐 ⧵𝑡) ∪ 𝑇𝑚 , ≺𝑐 ∪ ≺𝑚 ∪ ≺𝐷 ), where ≺𝐷 ∶= {(𝑡1 , 𝑡2 ) ∈ 𝑇𝑐 × 𝑇𝑚 |(𝑡1 , 𝑡) ∈≺𝑐 } ∪ {(𝑡1 , 𝑡2 ) ∈
𝑇𝑐 × 𝑇𝑚 |(𝑡, 𝑡2 ) ∈≺𝑐 }[6].
3.4. Planning problem and solution
A planning problem is a tuple (𝑄, 𝑂, 𝑀, 𝑡𝑛0 , 𝑠0 ), where:
• 𝑄 — a set of predicates;
• 𝑂 — a set of operators;
• 𝑀 — a set of methods;
• 𝑡𝑛0 — the initial network of tasks;
• 𝑠0 — the initial state.
A planning problem solution is a sequence of operators 𝑜1 , 𝑜2 , … , 𝑜𝑛 , that is executable in 𝑠0 .
That is, there exists a trajectory of states 𝑠1 , 𝑠2 , … , 𝑠𝑛 , such that 𝑜𝑖 is applicable to the state 𝑠𝑖−1 ,
and for any 𝑖 ∈ 1, … , 𝑛. The state 𝑠𝑖 is the result of applying 𝑜𝑖 to 𝑠𝑖−1 . The solution is found by
decomposing 𝑡𝑛0 [6].
4. Used technologies
To create a serious game about social engineering attacks the Unity game engine was used. The
choice of the game engine for this project was motivated by several considerations. First and
foremost, to make the game more immersive and interesting to play and study, it was deemed
necessary to create an imitation of a company or an organisation in a three dimensional envi-
ronment. One of the benefits of this approach is that it allows the visual representation of the
social engineering attacks. The Unity game engine supports three dimensional games which
can be created using Unity game editor. The versatility and usability of the editor helped to
concentrate on the development of game logic and social engineering attack imitation systems.
However, to implement completely a system for social engineering attacks imitation, it was
important to create a system that controls the behavior of non-player characters that would
imitate their meaningful activities. Since the imitation system was developed using Unity game
engine, the automatic planning system should have been integrated into this environment,
taking into account the features of the platform. In particular, taking into account that all
function calls in the Unity game engine are tied to certain events, like game scene initialization
or beginning of a frame rendering.
5. Implementation
A typical system implementation consists of 3 key components: agent, planner and task net-
work. All of these depend on the implementation of the game environment, however, the logic
behind task transition and decomposition can be made universal. Our GitHub project1 imple-
ments such logic.
1
Hierarchical Task Network planner for Unity. Simple scripts that help you to build AI
systems based on HTN planning. GitHub project page URL: https://github.com/KrylovBoris/
Hierarchical-Task-Network-planner-for-Unity
The UML class diagram depicted in figure 1 showcases the classes of the developed system.
Class Agent acts as the domain knowledge holder for the planning problem. The predicates
that describe preconditions of tasks and states of the environment are Boolean methods, which
serve as sensors of the agent. Using sensors, agents and their associated task networks per-
ceive the current state of the virtual environment. Sensors and agent actions are also provided
by the Agent class. As the implementation of these components may vary from one game
environment to another, it impossible to create a universal agent.
The HTN planner class is a scheduler in sense that it starts the task execution and handles
the situations when it is interrupted. For the different planning problems, the scheduler may
handle the interruptions differently, however out GitHub project page has an HTN planner
class example that can be used in other projects with minor alterations.
Classes Task, SimpleTask, ComplexTask and Plan are the system’s core components that are
handling the planning process and various condition checks.
Figure 1: The structure of classes included in the automatic planning system.
For the guidelines on how to implement Agent and HTN planner class, please, refer to the
project’s GitHub page.
5.1. Tasks
As can be seen from figure 1, composite and primitive tasks are the heirs of the Task class, that
implements the logic of checking preconditions, task execution, and also integrity rules for the
task.
Integrity rules are an extension of the classsic HTN planning method within our implemen-
tation, they are the special conditions that should not be violated throughout the entire time the
task is being executed. These are helpful when we want the continuous task to be interruptible.
While the task is executed, it take different states, such as:
• "Planned" is an initial state which indicates that the task is in the task network, but is
not being executed.
• "InProgress" is a state that shows that the task is being executed.
• The task is "Complete" after it is successfully executed and removed from the planning
queue.
• "Failed" state is assigned to the task when its preconditions are not met or some of the
integrity rules are violated.
5.2. Primitive tasks
The SimpleTask class implements logic related to operators and primitive tasks. Each instance
of the class calls an agent method, which changes the state of the environment. If the effect
of performing this action appears only after some time, we can postpone the transition to the
next task using completion conditions. The transition to a new task will not be carried out if
these conditions are not met.
To provide verification of completion conditions and task integrity rules, a Unity coroutine
is instantiated for each task in the system. Coroutine in Unity game engine is a function which
execution can be extended over several frames [24].
The effects in this model are not explicitly encoded, since a feature of the implemented
system is that the task network is decomposed until the first primitive task is received, which is
immediately executed, changing the state of the environment perceived by the agent’s sensors.
Such planning is called a plan search in the state space [18]. Thus, there is no need to encode
explicitly the effects of operators.
5.3. Compound tasks and task networks
Compound tasks are implemented in the ComplexTask class. Each instance of this class is
associated with an instance of the Plan class, which monitors the state of the task network and
controls the transition from one subtask in the network to another. Each plan contains a task
queue, which is formed on the basis of the task decomposition method.
Under the notion "task decomposition method" in this implementation is meant a function
that takes an input state of the environment and returns an array of tasks.
Each plan can take one of this three states:
• "InProgress";
• "Complete";
• "Failed".
These states partially correspond with the task state. That is because of the fact that each
ComplexTask instance state is tied to its dedicated Plan instance. While the plan is being
executed it is "InProgress". If the planning queue is successfully cleared, then the plan and the
task associated with that plan is "Complete". However, whilst executing the obtained plan, a
situation may arise when one task in the plan became "Failed". In this case, the entire structure
is assigned this status. Such cases when the execution of the plan is interrupted for some reason
are handled separately by the planner in accordance with the specifics of the implementation
of the environment. For example, the scheduler may restart the planning process after an error.
Also, this component of the system is responsible for the initial launch of the planning process
and the further behavior of the system after the successful completion of the plan.
More detailed description of the implementation can be found on the project’s GitHub page.
There you can also find documentation and simple tutorial on the integration of developed
system into your Unity project. It is should be pointed out that the project is still in development
and the project page will be updated in the future.
6. Graphical Notation
One of the most important tasks in the description of the planning problem is the description
of a domain knowledge in accordance with the given semantics. For the planning system de-
scribed in this paper, a task can be described using code, however, this leads to a large amount
of boilerplate code. In this regard, a graphical notation was developed using the Microsoft
Modeling SDK to make it possible to describe quickly planning tasks.
Figure 2 describes a simple planning problem using this notation. According to the compiled
description, C# language code is generated using the T4 text template.
Using this graphic language, one can describe sensor methods and agent action methods
(column “Agent Methods”). The specific implementation of these methods requires further
determination using C# programming language and can vary greatly depending on the envi-
ronment implemented on the Unity game engine. Primarily this graphic notation is designed
to be used for the description of primitive and composite tasks. In this notation, it is possible
to specify the preconditions, integrity rules and conditions defined in the first column. It can
also assign action methods to primitive tasks, and decomposition methods can be defined for
composite tasks.
Figure 2: Graphic notation used to describe the task of extracting the ball from a closed box.
As mentioned above, task decomposition methods are the functions that determine which
tasks a task should be decomposed into in a given state of the environment. To describe this
function, several blocks of various types are used: blocks of a beginning and an end of an algo-
rithm, task blocks (for determining an order of tasks in a resulting array) and branching blocks
(for changing a composition of a resulting array under certain conditions). It is also possible to
create recursive task networks by placing the composite task in the task decomposition method
associated with it. With their help, we can express cycles within task networks. Thus, using
the developed notation, all basic algorithmic structures can be expressed. One method of de-
composing a problem can encode several tuples (𝑡𝑐 (𝑚), 𝑓𝑝𝑟𝑒 (𝑜), 𝑡𝑛(𝑚)). It is important to note
that not all preconditions of the decomposition method are placed in the body of the function.
Preconditions common to all tuples are placed in the preconditions of the composite problem.
The usage of presented notation can save time spent on coding. It omits some implemen-
tation features, allowing to concentrate solely on designing the planning problem. The visual
nature of the notation and aforementioned omission allows users that has no experience in pro-
gramming to encode task networks. It can be extremely helpful for the diverse teams of game
developers. Game designers can design NPC behaviour using blocks of sensors and actions
that were prepared by the programmer.
But despite being a valuable asset during the development the serious game about social
engineering attacks, this extension requires some polishing before it can be properly shipped.
7. Conclusion
This article describes the development of the game artificial intelligence system based on hier-
archical task network planning, which is proposed to be used in a game dedicated to training
users to counteract social engineering attacks. A graphical notation for describing a planning
problem is presented. It is planned to finalize the graphical notation and ship a fleshed out
extension for the Visual Studio IDE in the future.
It also seems promising to create a graphical planning problem editor for the Unity game
editor, so that developers have the ability to edit agent behavior directly from Unity Editor.
This will allow the developers to efficiently encode their planning problems without tying
themselves to Visual Studio IDE.
Of the qualitative improvements of the system that are planned to be made in the future,
the support of the unordered sets of tasks can be noted. This change will increase the number
of different planning networks the developers can encode, while also reducing the amount of
code necessary for certain planning problems.
And finally, the possibility of transferring the implementation of the planning system to
other platforms is considered. Even though the current implementation relies on Unity Corou-
tines, other ways to check conditions can be implemented, making the system platform inde-
pendent.
Acknowledgments
This work was carried out as part of the project on state assignment SPIIRAS No. 0073-2019-
0003 and with financial support from the Russian Federal Property Fund (grants No. 18-01-
00626, No. 20-07-00839).
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