PRE2019 3 Group7

From Control Systems Technology Group
Revision as of 16:32, 13 February 2020 by S165480 (talk | contribs) (→‎Approach)
Jump to navigation Jump to search

Group Members

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

Subject

Old: Simulating populations using AI.

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

Old: Simulating populations can be useful in a lot of fields. A good-working simulation will therefore have a lot of users. Firstly, there are of course the biologists and behavioral analysts, who can perform experiments to try and understand populations better. A simulation like this can also be used in a population viability analysis, which is a species-specific method of risk assessment frequently used in conservation biology. Then we have the ecologists and government organisations who can use this tool for ecological risk assessment when they are making wildlife plans. Lastly, such a simulation can be useful in education to give students a better understanding of evolution and animal behaviour. (Avida-ED)

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

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

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

  • VORTEX: a computer simulation model for population viability analysis

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

  • 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

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

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

  • 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

  • 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

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

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

  • 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.

  • 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.

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.)

  • 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.

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.


[NEW SOURCES] [Daan]

  • 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

  • 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

  • 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

  • Effect of Alcohol References in Music on Alcohol Consumption in Public Drinking Places

https://onlinelibrary.wiley.com/doi/full/10.1111/j.1521-0391.2011.00182.x

  • 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

[Job]

  • Why Loud Music in Bars Increases Alcohol Consumption

https://www.spring.org.uk/2008/09/why-loud-music-in-bars-increases.php

  • Loud Music Is Scientifically Proven to Make You Drink More

https://www.digitalmusicnews.com/2017/11/14/loud-music-drinking/

  • Loud music in bars makes customers drink more, say scientists

https://www.telegraph.co.uk/news/2432699/Loud-music-in-bars-makes-customers-drink-more-say-scientists.html

  • Loud Music At Bars Makes You Drink 31% More Alcohol

https://wnaw.com/loud-music-at-bars-makes-you-drink-31-more-alcohol/

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

https://www.mdpi.com/1660-4601/9/11/4068/htm

  • Social Pressures to Drink or Drink a Little More: The Nigerian Experience

https://journals.sagepub.com/doi/abs/10.1177/009145090903600107

  • Come on, have a drink: The prevalence and cultural logic of social pressure to drink more

https://www.tandfonline.com/doi/full/10.1080/09687637.2016.1179718

  • Young people and alcohol: influences on how they drink

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

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

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

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

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

Wietske

Vrouwen drinken meer in mixed-gender groups en significant minder met alleen mannen dan met alleen vrouwen. Mannen drinken meer in mixed-gender groups met equal man/vrouw of mixed-gender groups met meer mannen, en minder met alleen vrouwen dan met alleen mannen.

    • 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;

    • Also: “The social context during a drinking occasion can impact upon drinking behaviour”: Monk R. L., Heim D. A systematic review of the alcohol norms literature: a focus on context. Drugs Educ Prev Policy 2014; 21: 263–282.

https://www.tandfonline.com/doi/full/10.3109/09687637.2014.899990

    • Also: “Previous studies found that a larger drinking‐group size was associated with heavier alcohol use among the group members” : Kairouz S., Gliksman L., Demers A., Adlaf E. M. For all these reasons, I do... drink: a multilevel analysis of contextual reasons for drinking among Canadian undergraduates. J Stud Alcohol 2002; 63: 600–608.

https://www.jsad.com/doi/abs/10.15288/jsa.2002.63.600 Reasons for drinking and the drinking setting together influence consumption. Moreover, reasons are context specific, because students drink for different reasons in different contexts. Thus, contextual motivational models may be more effective in helping one understand the various pathways to alcohol use and misuse.

    • En Senchak M., Leonard K. E., Greene B. W. Alcohol use among college students as a function of their typical social drinking context. Psychol Addict Behav 1998; 12: 62–70.

https://psycnet.apa.org/record/1998-00121-006

    • Also: “we have reported previously that the greater the number of friends present, the more drinks were consumed at any given hour during the course of the evening”: Thrul J., Kuntsche E. The impact of friends on young adults’ drinking over the course of the evening—an event‐level analysis. Addiction 2015; 110: 619–626.

https://onlinelibrary.wiley.com/doi/full/10.1111/add.12862 The higher the number of friends present, the higher the number of drinks consumed at a given time during the course of the evening

Scents: orange, seawater, and peppermint were tried, no significant differences were found between the scents. The scents did enhance dancing activity and improve the evaluation of the evening. GEUR IMPROVED DE AVOND

  • 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.

  • Comparing nightclub customers’ preferences in existing and emerging markets (2007):

https://www.sciencedirect.com/science/article/pii/S0278431906001307 Zoekt uit wat Britten en Polen belangrijk vinden in nightclubs.

  • 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.

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.

  • ‘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.

  • Nightclubs and bars: what docustomers really want? (2005)

https://www.emerald.com/insight/content/doi/10.1108/09596110510582314/full/pdf?title=nightclubs-and-bars-what-do-customers-really-want Titel is vanzelfsprekend. This paper aims to give a wider understanding of what customers really want from first and subsequent visits to mainstream city centre nightclubs and bars by examining customer attitudes to various aspects of the services arena and service offerings provided by such venues.

  • 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).

[Jasper]

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

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

  • SOUND LEVEL OF BACKGROUND MUSIC AND ALCOHOL CONSUMPTION: AN EMPIRICAL EVALUATION

https://journals.sagepub.com/doi/pdf/10.2466/pms.99.1.34-38

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

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

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

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

  • Loud Music Can Make You Drink More, In Less Time, In A Bar

https://www.sciencedaily.com/releases/2008/07/080718180723.htm

  • 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

Approach

Old: 1 Create a simulated environment where AI based on neural networks can roam.

2 Edit factors such as amount of food and maximum speed of the AI to see how this influences the AI.

3 Analyze the results of step 2, iterate and try to find interesting stuff.

4 Document everything interesting

New:

1.Determine important variable for simulation

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

3.Determine holes in correlations, research those correlations.

3.1
a)Corralation between dancing and alcohol consumption.
b)Time between no alcohol in hand and new alcohol consumption.
c)Correletion sfeer and alcohol consumption (adje voor de sfeer, shots etc)
3.2 Watch people at stratum. Note down the times they get new alcohol and the reasons for finishing a drink with timestamps.
3.3 Analyze the data create estimations for the correlations of 3.1

Planning

  • Week 1: research state-of-the-art, finalise plan
  • Week 2: More research, state requirements for simulation
  • 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

Milestones

Understanding of state of the art

A list of features we might want to implement into our simulation

A minimal viable product: a simulation that can house AI based on neural networks

The simulation but with some of the features of the list above implemented

Having research results by watching the simulations

Having research results by changing some factors of the simulation (such as amount of food)

Documenting our results and comparing them to real life to create a conclusion

Deliverables

  • A simulated environment where AI based on neural networks can roam.
  • This wiki page, which contains our process, research and the results of our analysis.
  • A presentation

Who is doing what

Week 1

Name Total Break-down
All 2h Discussing the subject
Daan
Job
Sanne 3h Making a draft for the wiki (0.5 h), Gathering links for the State of the Art (2.5h)
Jasper 1h Gathering articles for state-of-the-art (1.5 h)
Wietske 4h Working on the wiki (0.5 h), Gathering articles for state-of-the-art (3.5 h)

Week 2

Name Total Break-down
All 4h Discussing the subject
Daan 2h Working on the wiki (2 h)
Job 2h Working on the wiki (2 h)
Sanne
Jasper
Wietske
Retrieved from "https://dsdwiki.wtb.tue.nl/index.php?title=PRE2019_3_Group7&oldid=83025"