PRE2019 3 Group7

Group Members

Name	Study	Student ID
Daan Schalk		0962457
Job Willems		1003011
Jasper Dellaert		1252454
Sanne van Wijk		1018078
Wietske Blijjenberg		1025111

Subject

In the UK, the late night culture of “clubbing” is very popular with around 40% of the key target group being students. In the UK, the average club derives around two-thirds of tis revenue from the sale of beverages, and it is therefore a vital source of profits .

New: Simulate the dancing behaviour of people in a bar or club.

When looking at Stratumseind these days, clubs and bars that were once flourishing look empty and silent. For a manager of such a bar, it may be difficult to decide what to improve to regain the glory of those golden days. Humans are complex, so their happiness and willingness to spend may depend on different factors combined in a specific way. Because of all these different factors, experimenting with them in real life is impossible work. On top of that, change in revenue might not be seen in a week, to be sure a longer observation period is needed. However, if you need to test a lot of different factors in a lot of different combinations, this takes ages.

That is where a simulation of the behaviours of the people in a bar or club might help. The simulation will make it easy to experiment with different factors, and adds the possibility to run multiple simulation in sequence. This way, certainty can be reached about the influence of a certain factor on the revenue, and managers of clubs and bars can come to decisions about what to improve to make their bar great again.

Objectives

• Main goal: assist the owner of a bar or dance hall with ideas on how to improve customer revenue.
• Construct an AI multi-agent simulation with which we can find what variables with what capacities are needed to maximize generated income.
• Analyse the reliability of such a simulation by discussing the observations of the client and the results of previous research done in this field (if any apply).
• Analyse the possibilities and shortcomings of such a simulation.

Research variables

General variables

• Income: The amount of money spent by patrons in order to buy alcohol
• music: Is there music present? if so, how loud is the music currently?
• Crowded level: How crowded is the bar/dance hall
• Dancing crowd: How many patron are currently dancing?

Patron specific variables

• Money: What amount of money do the patrons carry?
• Willingness to pay: How easy do the patrons part with their money in exchange for alcohol?
• Has alcohol: Patrons can get alcohol in exchange for money, which will slowly intoxicate them when consumed
• Intoxication: Intoxication changes the behaviour of a patron
• Alcohol tolerance: Patrons can endure only a specified amount of alcohol
• crowd sizes: In what group sizes can the patrons arrive and converse?
• Comfortability: Does the patron currently feel at ease?
• Dance affinity: Does the patron like to dance?
• Energy: All actions that a patron takes cost some energy.

Patron states

• Drinking: The only state that can be done simultaneously with any of the other states: In this state, the patron's alchohol will be drunk, increasing its intoxication level.
• Dancing: The patron is currently dancing and socialising with others. This actions does cost more energy than any other action.
• Talking: The patron is conversing with others, This does cost some energy.
• Buying: The patron is currently buying more alcohol for him/herself.
• Standing: In this state, the patron is inactive and unsocial. This state barely costs any energy.

Users

New (10/2): This simulator should prove useful to bar, club, dance or music events managers. Since this will help them with determining how to best get people dancing. Which for these managers in turn will mean more income. This could possible be used for other people who want to organize a personal event.

A secondary user for which this simulator might prove useful, are the people going to the bars, clubs, dance or music events. Assuming that customers spend more money when they enjoy themselves and leave when they do not, using the results of the simulator to improve bars would mean that bars become more enjoyable. This is beneficial for the customers: they will have a better time.

State-of-the-art

AI and simulations

• Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

https://www.sciencedirect.com/science/article/abs/pii/S0378475408000505

• Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence (2018)

https://www.nature.com/articles/s41370-018-0052-y/ Context: HUMANS exposure to a chemical. Because descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments, and the existing method is difficult and labor-intensive, a simulation of longitudinal patterns in human behaviour was created. This is an agent-based model with a needs-based AI. Needs-based because humans make their decisions to take actions in order to fulfil needs. The paper describes how it is implemented. Meets critical need in field of exposure assessment. Only addresses a few needs, and not the complex ones.

• this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress.

https://ieeexplore.ieee.org/abstract/document/4141061

• This paper describes a novel system for creating virtual creatures that move and behave in simulated three-dimensional physical worlds. A genetic language is presented that uses nodes and connections as its primitive elements to represent directed graphs, which are used to describe both the morphology and the neural circuitry of these creatures.

http://www.karlsims.com/papers/siggraph94.pdf

• This paper explores selecting for evolvability in neural networks. Evolvability Search enables generating evolvability more easily and directly, facilitating its study and understanding, and may inspire future practical algorithms that increase evolvability without significant computational overhead.

http://www.evolvingai.org/mengistu-lehman-clune-2016-evolvability-search-directly

• In this paper digital organisms were used to investigate the ability of natural selection to adjust and optimize mutation rates.

http://www.evolvingai.org/clune-misevic-ofria-lenski-2008-natural-selection-fails

• This paper explores novelty search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. A common criticism of Novelty Search is that it is effectively random or exhaustive search because it tries solutions in an unordered manner until a correct one is found. Its creators respond that over time Novelty Search accumulates information about the environment in the form of skills relevant to reaching uncharted territory, but to date no evidence for that hypothesis has been presented.

http://www.evolvingai.org/velez-clune-2014-novelty-search-creates-robots

• Evolutionary computing (2002)

https://www.cs.vu.nl/~gusz/papers/ec-intro-Eiben-Schoenauer.pdf This paper gives a general overview into evolutionary computing.

• Introduction to evolutionary computing (2003)

http://cslt.riit.tsinghua.edu.cn/mediawiki/images/e/e8/Introduction_to_Evolutionary_Computing.pdf This book gives an insight into how evolutionary computing works and how it can be implemented.

MABS

• Multi-agent Based Simulation: Where Are the Agents?

https://link.springer.com/chapter/10.1007/3-540-36483-8_1

• MABE (Modular Agent Based Evolver): A framework for digital evolution research (2017)

https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_016 MABE is a modular and reconfigurable digital evolution research tool designed to minimize the time from hypotheses generation to hypotheses testing. MABE provides an accessible framework which seeks to increase collaborations and to facilitate reuse by implementing only features that are common to most experiments, while leaving experimentally dependent details up to the user. "One difficulty in Digital Evolution research stems from the need to develop the software used to conduct the re-search"

• Artificial Intelligence Techniques to Enhance Actors’ Decision Strategies in Socio-­ecological Agent-­ Based Models (2016)

https://scholarsarchive.byu.edu/iemssconference/2016/Stream-D/19/ Title is pretty self-explanatory. Provides an analysis of the types of AI learning algorithms employed in various application domains which use Agent-Based Models, their specific operationalization in an agent’s decision-­making for various tasks, treatment of spatial and social environment in the design of AI learning algorithms, and the level of empirical information used in ABM. Also highlights the trends in the current practice of AI learning algorithms used to enhance ABMs.

• Agent-based model calibration using machine learning surrogates (2018)

https://www.sciencedirect.com/science/article/pii/S0165188918301088 Tackles parameter space exploration and calibration of agent based models by combining machine-learning and intelligent iterative sampling. Results domanstrate that machine learning surrogates obtained using the proposed iterative learning procedure provide a quite accurate proxy of the true model and dramatically reduce the computation time necessary for large scale parameter space exploration and calibration.

• Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2012)

https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210x.12002# Exactly as the title suggests. Suggestion and evaluation of how to model animal energy needs in agent-based models.

• Using stylized agent-based models for population–environment research: a case study from the Galápagos Islands (2010)

https://link.springer.com/article/10.1007/s11111-010-0110-4 More about the utility of ABM's : here they are named useful for sharpening conceptualizations of population–environment systems, testing alternative scenarios, and uncovering critical data gaps. (Also about trade-offs between model complexity and abstraction.)

Specific animal population simulation

• A Generalized Computer Simulation Model for Fish Population Studies

https://afspubs.onlinelibrary.wiley.com/doi/abs/10.1577/1548-8659(1969)98[505:AGCSMF]2.0.CO;2

• Application of Multi-agent Simulation in Animal Epidemic Emergency Management: Take an Example of AFS (Africa Fever Swine) Policy

http://www.dpi-proceedings.com/index.php/dtetr/article/view/31843

• VORTEX: a computer simulation model for population viability analysis

https://www.publish.csiro.au/WR/WR9930045

• An artificial intelligence modelling approach to simulating animal habitat interactions

https://www.researchgate.net/profile/Jane_Packard2/publication/237332438_An_artificial_intelligence_modelling_approach_to_simulating_animalhabitat_interactions/links/5b882577a6fdcc5f8b72005e/An-artificial-intelligence-modelling-approach-to-simulating-animal-habitat-interactions.pdf

• An overview of a simulation of an ecosystem housing predators and prey. The simulation has much hard coded behavior, allowing the simulation to get more realistic.

https://www.youtube.com/watch?v=r_It_X7v-1E

Populations

• An overview of a simulation of an ecosystem housing creatures based on neural networks. With robust neural networks and no hard coded behaviours, this simulation allows for more emergent behaviour and potential realism at the cost of current realism.

https://www.youtube.com/watch?v=myJ7YOZGkv0

• An overview of a simulation of an ecosystem with a complex environment. This AI has hard coded features for interacting with the environment, however it can still evolve a neural network, striking a balance between the previous two simulations.

https://www.youtube.com/watch?v=E-zcUzK8k7U

• A Study of AI Population Dynamics with Million-agent Reinforcement Learning (2018)

https://dl.acm.org/doi/10.5555/3237383.3238096

• Population based training of neural networks. Population based training discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training.

https://arxiv.org/abs/1711.09846 (the paper)

https://deepmind.com/blog/article/population-based-training-neural-networks (a blogpost about the paper)

https://www.youtube.com/watch?v=l-Ga0E9vldg (a talk about the paper)

• A talk by Jeff Clune (http://jeffclune.com/) about recent (2019) avancements in population-based search. Focusing on explicitly searching for behavioral diversity, open-ended search and indirect encoding.

https://www.youtube.com/watch?v=g6HiuEnbwJE

• Exploring the Relationship between Experiences with Digital Evolution and Students' Scientific Understanding and Acceptance of Evolution (2018)

https://avida-ed.msu.edu/files/curricula/ABT_Exploring_Relationship__Understanding_Acceptance_Evo.pdf Uses a research-based platform for digital evolution in the classroom, found that engagement in lessons with Avida-ED both supported studentlearning of fundamental evolution concepts and was associated with an increase in student acceptance of evolution as evidence-based science. Also found a significant, positive association between increased understanding and acceptance. --> arguments for education as one of the users

• Effects of mass extinction on community stability and emergence of coordinated stasis with digital evolution (2018)

http://en.cnki.com.cn/Article_en/CJFDTotal-NJNY201801012.htm Research based on digital evolution, can be used as application example.

Ecological risk assessment

• Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery

https://www.sciencedirect.com/science/article/pii/S0160412016300824 There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance – whole organisms, populations, communities, and ecosystems – are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.

• The role of agent-based models in wildlife ecology and management (2011)

https://www.sciencedirect.com/science/article/pii/S0304380011000524 Wildlife-management ABMs disentangle habitat use and quality, and represent dynamic environments. Using adaptive movement ecology in changing landscapes permits scenario planning of future habitats. ABMs are excellent tools encompassing multiple disciplines and stakeholder interests. Can be used to substantiate arguments about why evolution simulations are useful in wildlife ecology and management.

Articles about alcohol effect on human behavior

• Functional Benefits of (Modest) Alcohol Consumption (2017)

https://link.springer.com/article/10.1007/s40750-016-0058-4 Alcohol might increase the degree of social bonding, and this might have implications for how happy and socially engaged people become. --> not super useful.

• Alcohol effects on human risk taking (2003)

https://link.springer.com/article/10.1007/s00213-003-1628-2 Alcohol intake can contribute to human risk taking.

• “A Cool Little Buzz”: Alcohol Intoxication in the Dance Club Scene (2014)

https://www.tandfonline.com/doi/abs/10.3109/10826084.2013.852582?casa_token=K40lVC-o6VIAAAAA:lIK3Ny1gk_y54GcqLp23gwXx8cInC2zUNeR2CyOLfVXyFPg0jUh_NFBaCVMPt-DOHStljPN1U2h3fA About young adult Asian Americans in the dance club scene. Describes: Alcohol Intoxication and Sociability, Being Intoxicated is Fun, Degrees of Intoxication: Getting Buzzed, not Drunk,

• DELAY OR PROBABILITY DISCOUNTING IN A MODEL OF IMPULSIVE BEHAVIOR: EFFECT OF ALCOHOL (2013)

https://onlinelibrary.wiley.com/doi/epdf/10.1901/jeab.1999.71-121

• Behavioural correlates of alcohol intoxication (1993)

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1360-0443.1993.tb02761.x

• ALCOHOL INTOXICATION REDUCES IMPULSIVITY IN THE DELAY-DISCOUNTING PARADIGM (2003)

https://academic.oup.com/alcalc/article/38/2/151/195854 Alcohol intoxication does not always increase cognitive impulsivity and may lead to more cautious decision-making under certain conditions.

Alcohol behaviour and social settings

• The effects of alcohol expectancies on drinking behaviour in peer groups: observations in a naturalistic setting

https://onlinelibrary.wiley.com/doi/full/10.1111/j.1360-0443.2005.01152.x

Social groups and their influence on the drinking behaviour of people in a bar

One can imagine that the composition of ones drinking group can have quite an influence on how you drink. Several studies support that indeed, the social context of a drinking occasion can impact drinking behaviour . Several studies shed light on more specific social factors and their corresponding impacts.

For starters, a larger drinking-group size is associated with heavier drinking . This seems quite logical: with more people, there are more people doing rounds, thus there is a longer time between having to get alcohol yourself while still getting a continuous supply of alcohol. Interestingly, this relationship was stronger for man than for woman . Also, man were found to consume more alcohol than woman, particularly at the beginnings of the evening. .

On top of the number of friends you take with you, the gender composition of those friends also influences drinking behaviour. A study found that both males and females consume significantly more drinks in mixed-gender groups. . There is a difference between males and females here: men consumed more drinks in groups with an equal amount of males and females and groups with men in the majority. They also consumed more drinks with in a group with men only compared to woman only.

Woman however, although they did consume more drinks in mixed-gender groups, consumed significantly fewer drinks in groups with men only than groups with woman only.

There are more differences between male and female pub-goers. One study found that male friends or acquaintances were the main sources of pressure on people to drink or drink more . According to the same paper, pressure to drink also depends on religion and gender. Another study found that indirect pressures to drink play a more significant role than direct pressures

Male/female

• The male Suburban pub-goer and the Meaning structure of drinking

https://journals.sagepub.com/doi/abs/10.1177/000169938502800202?journalCode=asja

• Darts, Drink and the Pub: The Culture of Female Drinking

https://journals.sagepub.com/doi/abs/10.1111/j.1467-954X.1987.tb00557.x

• Genderedness of bar drinking culture and alcohol-related harms: A multi-country study

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660036/

• Men and Women: Do They Value the Same Things in Mainstream Nightclubs and Bars? (2009): https://journals.sagepub.com/doi/abs/10.1057/thr.2008.37

Titel is vanzelfsprekend. Results: er zijn verschillen maar ook overeenkomsten.

• ‘I go to dance, right?’: representation/sensation on the gendered dance floor (2012):

https://www.tandfonline.com/doi/full/10.1080/02614367.2013.798348 This article explores a group of young adults’ experiences of social dancing in the Eastside of Vancouver, BC, Canada.

• Kijk naar: “Occurs primarily on Friday and Saturday nights”: Kuntsche E., Gmel G. Alcohol consumption in late adolescence and early adulthood—where is the problem? Swiss Med Wkly 2013; 143: w13826.

https://serval.unil.ch/notice/serval:BIB_93F9E1EBAF0B RSOD is by far most prevalent on Saturday evenings followed by Friday evenings, usually because young people go out and do not have any work or study responsibilities the next day;

Music

• The Impact of the Bass Drum on Human Dance Movement (2012) :

https://mp.ucpress.edu/content/30/4/349.abstract Promincence of the bass drum in contemporary dance music has strong influence on dancing itself. People modify their bodily behaviour according to the dynamic level of the bass drum. Participants moved more actively and displayed a higher degree of tempo entrainment as the sound pressure level of the bass drum increased.

• The effect of tempo of background music on duration of stay and spending in a bar

https://jyx.jyu.fi/bitstream/handle/123456789/20304/URN_NBN_fi_jyu-200905271640.pdf?sequence=1

Music and its influence on the behaviour of people in a bar

Music can have quite an influence on how people behave in social settings. Most people like background music: studies have found that people spend less time drinking in bars that don’t play music . It goes even further: according to a previous study, loud music makes people drink 31% more . The reason behind this is could be that when people can not communicate due to the noise in the bar, they start focussing on drinking . Another explanation is that high sound levels may cause higher arousal, which leads the subjects to drink faster and order more drinks .

Another study links faster music to faster drinking . However, a different study links slower music to more sales. Customers stayed about the same amount of time but spent more during slow music than during normal or fast music. . An explanation for this difference is that people prefer to listen to music that moderates their state of arousal . During a relaxing activity like an afternoon beer, people prefer slow low-arousal music, while during a night out people prefer music that further heightens their state of arousal. On top of that, studies found that the genre of music matters. Jacob (2006) found that when playing drinking songs in a bar, the duration of stay and spending both are increased . Further research supported this and claimed that customers exposed to textual references to alcohol spent significantly more on alcoholic drinks than those who were not

Environment related factors (smell, type of bar, etcetera)

Other factors

Apart from the earlier named influences on alcohol intake, a lot of other factors can influence how people behave on a night out.

One of these factors is scent: on a night out, one may encounter many scents, both pleasant and vile. Sweating bodies, alcohol, you name it. It would seem logical that ambient scents that mask the vile odours could contribute positively to the night-time experience. A previous study confirms this: they tested the scents of orange, seawater and peppermint, and found that all scents enhanced dancing activity and improved the evaluation of the evening, music and mood . Additionally, they found that the increase in dancing coincided with an increase in temperature in the club. This may lead to the customers wanting more alcohol: if it is hot, what’s better than a cold beer to cool off?

Factors that influence how much people like a bar

Apart from knowing how to influence the alcohol intake of customers, it is important to know how to get customers. There have been a few studies that worked with focus groups in order to find out what people wanted from bars. First of all we have security. Most bars have security, if only to dismiss all people below legal drinking age. Studies found that it matters how the security looks: a more formal attire offers a greater image of security . This is because people do not like going somewhere where you do not feel safe. People however wanted the security personal to look friendly as well, they did not want to feel threatened by the very security that should protect them . The clientele is important as well. People prefer a mix of male and female clientele, with the majority liking a male-dominated place the least . This seems to corollate with the fact that people drink more in mixed-gender groups. Males offered an explanation for their preference for mixed-gender bars: they identified the opposite sex as main reason to go to a bar . Then we have the seating. Opposing the stereotypical image of pub customers on barstools, the same study found that sofas are the most preferred as seating arrangement inside a venue. Seating and bar stools proved far less popular. People like relaxing and taking a break from the pushing and shoving every now and then .

• Drunk and Disorganised: Relationships between Bar Characteristics and Customer Intoxication in European Drinking Environments

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3524613/

Overig

• Craving and Attentional Bias Respond Differently to Alcohol Priming: A Field Study in the Pub

https://www.karger.com/Article/Abstract/253859

• Pastime in a pub: Observations of young adults' activities and alcohol consumption

https://www.sciencedirect.com/science/article/pii/S0306460306001638

• Visiting Public Drinking Places: An Explorative Study into the Functions of Pub-Going for Late Adolescents

https://www.tandfonline.com/doi/abs/10.3109/10826089909039408

• Reporting on responsible drinking: a study of the major UK pub‐owning companies

https://onlinelibrary.wiley.com/doi/full/10.1111/j.1467-8608.2006.00469.x

• Young people and alcohol: influences on how they drink

http://www.ias.org.uk/uploads/pdf/Young%20people/alcohol-young-adults-summary.pdf

• Why Do You Dance? Development of the Dance Motivation Inventory (DMI)

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122866

• Spatiotemporal variations in nightlife consumption: A comparison of students in two Dutch cities

https://www.sciencedirect.com/science/article/pii/S0143622814001647 Important between and within city differences exist in nightlife consumption. Typologies of city-centre nightlife consumption patterns are created. Participation in different patterns is most strongly shaped by level of education. Contrary to popular discourses not all patterns involve excessive alcohol consumption. Alcohol consumption needs to be seen as part of the social practice of going out.

• ‘That right level of intoxication’: A Grounded Theory study on young adults’ drinking in nightlife settings (2014):

https://www.tandfonline.com/doi/full/10.1080/13676261.2015.1059931 The present study examined the meaning and functions of drinking across different nightlife settings (e.g., bars, dance clubs) in a sample of Italian young adults. Results indicated that three major categories of social nightlife settings associated with different meanings and uses of alcohol: a more moderate social drinking in bars, a pursuit of a desired level of intoxication in dancing settings, like nightclubs, with festivities and celebratory settings most associated with alcohol abuse and heavy drunkenness as a mean to maximize the celebration and the uniqueness of the event.

• Measuring College Students' Alcohol Consumption in Natural Drinking Environments: Field Methodologies for Bars and Parties (2007):

https://journals.sagepub.com/doi/abs/10.1177/0193841X07303582 This article presents field methodologies for measuring college students' alcohol consumption in natural drinking environments. Specifically, we present the methodology from a large field study of student drinking environments along with some illustrative data from the same study.

• Blood alcohol concentrations among bar patrons: A multi-level study of drinking behaviour (2008):

https://www.sciencedirect.com/science/article/pii/S0376871609000325 The study examines: (1) drinking behavior and settings prior to going to a bar; (2) characteristics of the bar where respondents are drinking; (3) person and environmental predictors of BrAC (blood alcohol concentration) change (entrance to exit).

• Cognitive Performance Measured on the Ascending and Descending Limb of the Blood Alcohol Curve

https://link.springer.com/content/pdf/10.1007%2FBF00401185.pdf

• THE EFFECT OF ALCOHOL CONSUMPTION ON RISK-TAKING WHILE DRIVING

https://www.sciencedirect.com/science/article/pii/0306460387900347

Approach

1.Determine important variable for simulation

2. Research state of the art.

2.1 Determine correlations between the determined variables and alcohol consumption trough state of the art.

2.2 formalyze hypothesis etc.

3.Determine unknowns in correlations, research those correlations.

3.1 Set up a research plan.

3.2 Execute the research plan.

3.3 Analyze the results.

4. Set up a simulation using the found correlations.

4.1 Create a minimum viable product: a bar setting with AI using that bar.

4.2 start implementen each of the correlations found in 2 and 3.

5 Analyze the simulation

5.1 Change variables, optimize the simulation.

5.2 Refer back to research and hypothesis, is our simulation realistic and how does it comply with our hypothesis?

5.3 If neccecary review steps 2, 3 and 4.

6 Finalize wiki and conclusions

Planning

• Week 1: research state-of-the-art, finalise plan
• Week 2: More research, state requirements for simulation, create research plan for filling in the blanks of the state-of-the-art.
• Week 3: Implemented first version of simulation with only basic features
• Week 4: Implemented second version simulation with requirements implemented
• Week 5: Performing simulation, documenting results
• Week 6: Performing simulation, documenting results
• Week 7: Compare results to real life, create conclusion
• Week 8: Finalise wiki

Gantt chart

Milestones

Tasks	Estimated Time
Planning	12:00
State-Of-The-Art research	75:00
Conclusions state of the art	5:00
Set up research plan	5:00
Execute research plan	50:00
Analyze results	20:00
Minimum viable product	50:00
Simulation with more complex AI	100:00
Analyze simulation	75:00
Update research simulation based on results	50:00
Write down conlcusions	20:00
Finalize wiki	20:00
Create presentation	10:00
Unforseen	100:00
Total	592:00

Deliverables

State-of-the-art

A list of variables influencing behaviour at a bar setting.

A list of missing variables influencing behaviour at a bar setting.

A hypothesis on how the results our simulation would create.

A research plan detailing how to fill in the blanks of the state of the art.

Results of our research.

A minimum viable product: a simulation that can houses AI that can navigate a bar setting.

Extension of the minimum viable product trough implementing more complex behaviours found in the research.

Results out of the simulation, a set of factors and the behaviour it creates in the simulation.

Finalized wiki and conclusions.

A presentation

Who is doing what

Week 1

Name	Total	Break-down
Daan	2h	Discussing the subject
Job	2h	Discussing the subject
Sanne	5h	Making a draft for the wiki (0.5 h), Gathering links for the State of the Art (2.5h), Discussing the subject (2h)
Jasper	3h	Gathering articles for state-of-the-art (1.5 h), Discussing the subject (2h)
Wietske	6h	Working on the wiki (0.5 h), Gathering articles for state-of-the-art (3.5 h), Discussing the subject (2h)

Week 2

Name	Total	Break-down
Daan	10h	Working on the wiki (3 h), gathering articles for state-of-the-art (3 h), Discussing the subject (4h)
Job	10h	Working on the wiki (2 h), gathering articles for state-of-the-art (4 h), Discussing the subject (4h)
Sanne	10h	Working on the approach and planning (3h), working on the state of the art (3h) Discussing the subject (4h)
Jasper	9h	Wokring on the wiki (1 h), gathering articles for state-of-the-art (4h) Discussing the subject (4h)
Wietske	11.5 h	Working on the wiki (1 h), discussing the subject (4h), gathering articles for state-of-the-art (3 h), organizing sources on wiki by subject (1h), writing about the effect of music (1h), writing about social groups and alcohol(1.5 h)