Concurrent Hierarchical Finite State Machines for Modeling Pedestrian Behavioral Tendencies

Kielar, Peter M.; Handel, Oliver; Biedermann, Daniel H.; Borrmann, André

doi:10.1016/j.trpro.2014.09.098

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…There exists already work which tries to cope with psychological factors and suggest models, like the PECS Model [13], PMServ [14] or a concurrent hierarchical state machine [15]. The PECS model is capable of modelling physical, emotional, cognitive and social factors and originates in Operations Research.…”

Section: State-of-the-art Models: What Are They Capable Of and What Wmentioning

confidence: 99%

“…The MACES (Multi-Agent Communication for Evacuation Simulation) in combination with PMServ (psychological representation with memory data in combination with a short-term memory system) is an architecture to integrate a psychological model into crowd simulations [14]. The authors of [15] propose a concurrent hierarchical finite state machine for modelling pedestrian behavioural tendencies.…”

Section: State-of-the-art Models: What Are They Capable Of and What Wmentioning

confidence: 99%

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Bridging the gap Why we need to enhance current simulation models

Angelika¹

2020

Coll Dyn

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Many models that simulate evacuations are state of the art and provide realistic insight to their users. However, simulating everyday situations, such as visitor flow through a museum or passenger flow through an airport, presents marked challenges; existing models reach their limit here. This contribution will introduce and highlight the gap between existing egress models and the difficulties found simulating, for instance, passenger flow or capacity analysis.

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Section: State-of-the-art Models: What Are They Capable Of and What Wmentioning

confidence: 99%

Section: State-of-the-art Models: What Are They Capable Of and What Wmentioning

confidence: 99%

Bridging the gap Why we need to enhance current simulation models

Angelika¹

2020

Coll Dyn

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“…Automata may be implemented in different forms in streetscape models. Finite state machines [299] often use events as triggers for transition between states [300,301]. Agent automata [302,303] can ascribe human agency to agent-characters [302,304,305] or functional agency to urban forms [306].…”

Section: Data Modelsmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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“…Meanwhile, HFSM as a developmental method of FSM has the advantage of reducing the complexity of designing agent behavior, which can simplify the computational process and keep the constraint by minimizing a number of coding bits and adjusting the codes for the states [2,3]. A detailed and realistic simulation of dynamic behavior requires a precise modeling of decision-making process regarding action prioritization and selection, which is capable to develop goal directed behavioral tendencies and tactical model based on demands and input parameter [4]. Importantly, information is a basic requirement for the development process as a part of the ecosystem [5], which can be utilized lead to the substantial savings in power consumption [6], differentiate distinctive attributes of behavior [7], develop collaboration at tactical level [8,20], etc.…”

Section: Introductionmentioning

confidence: 99%

Defense behavior of real time strategy games: comparison between HFSM and FSM

Fauzi

Hariadi

Lubis

et al. 2019

IJEECS

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<span>RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.</span>

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Concurrent Hierarchical Finite State Machines for Modeling Pedestrian Behavioral Tendencies

Cited by 14 publications

References 16 publications

Bridging the gap Why we need to enhance current simulation models

Bridging the gap Why we need to enhance current simulation models

Computational Streetscapes

Defense behavior of real time strategy games: comparison between HFSM and FSM

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