1. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 1 Ognen Paunovski, George Eleftherakis and Tony Cowling FUZZY APPROACH TO DETECTION OF EMERGENT HERD FORMATIONS IN MULTI-AGENT SIMULATION Ognen Paunovski (ogpaunovski@seerc.org) South-East European Research Centre (SEERC) George Eleftherakis (eleftherakis@city.academic.gr) Department of Computer Science, City College Tony Cowling (a.cowling@dcs.shef.ac.uk) Department of Computer Science, University of Sheffield

2. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 2 Ognen Paunovski, George Eleftherakis and Tony Cowling Presentation Outline PART I: Simulation Model –Theoretical Background for the Model –Individual Animal and MAS model PART II: The Herd Pattern –Herd as Emergent Phenomena –Herd as Pattern Recognition Problem –Requirements for Herd Detection Algorithm PART III: The Automated Herd Detection Mechanism –Reasoning for Herd Detection –Overview of the algorithm of the fuzzy reasoner –Reasoning about an individual –Transition from individual to group –Reasoning about a group PART IV: Summary and Future Work

3. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 3 Ognen Paunovski, George Eleftherakis and Tony Cowling PART I Simulation Model

4. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 4 Ognen Paunovski, George Eleftherakis and Tony Cowling Simulation Model – Influence Zones The model is based on animal interaction through influence zones supported in a study done by Gueron, Levin and Rubenstein “The Dynamics of Herds: From Individuals to Aggregations” Stress zone - The need for personal space causes the animal to be repelled by its neighbours when they infringe its personal zone. Neutral zone - In the neutral zone an animal does not react to neighbours. Attraction zone – When neighbours are in the attraction zone the animal moves towards them.

5. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 5 Ognen Paunovski, George Eleftherakis and Tony Cowling Simulation Model – agent’s X-machine model - The states represent the movement direction and speed at particular time instance. - The transitions represent a change in the animal’s movement speed or direction. Fig. Diagrammatic X-machine model of an animal

6. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 6 Ognen Paunovski, George Eleftherakis and Tony Cowling Simulation Model – NetLogo Simulation Environment NetLogo was selected as multi-agent simulation environment Visual animator for observing the herd aggregations Data output mechanisms Correspondence between NetLogo script and XMDL Screen capture of the Herd Formation model in NetLogo during execution

7. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 7 Ognen Paunovski, George Eleftherakis and Tony Cowling PART II The Herd Pattern

8. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 8 Ognen Paunovski, George Eleftherakis and Tony Cowling Herd as Emergent Phenomena AIM OF THE STUDY: To explore of causal micro/macro relations in complex systems exhibiting emergent phenomena like in the case of herd dynamics. Group formation is a common characteristic for many social animals: fish form schools, birds form flocks and some mammals form herds. Is herd formation an emergent phenomenon? The formation of herd is not a surprise to the observer, the study of the phenomenon has determined a set rules to form a herd. However: There is nothing to suggest in a single animal that when in large numbers these animals would form a herd. Observations suggest that both small and large groups rely on local coordination between the individuals to form a herd The herd has functional significance to the group, it reduces the predation risk, increases the opportunities for mating.

9. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 9 Ognen Paunovski, George Eleftherakis and Tony Cowling Herd as Pattern Recognition Pattern is an entity, that can be distinguished from the chaos. Herd is a pattern. Pattern recognition is essentially a classification process dealing with the identification of patterns. Pattern recognition methods can be supervised and unsupervised. Pattern recognition methods: –Template matching is essentially a procedure where a pattern is compared against a prototype (template). –Syntactic approaches rely on a primitive set of sub-patterns and a set of rules which define how to compose (or decompose) more complex patterns. –Neural networks adopt ideas from biological systems in order to resolve complex non-linear problems. –Statistical methods conceptualize a “pattern” in terms of multidimensional spatial measures, which represent the features of an entity to be classified.

10. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 10 Ognen Paunovski, George Eleftherakis and Tony Cowling Requirements for Herd Detection Algorithm The nature of the herd dynamics study imposed several requirements for the clustering process: should be done continuously during the model execution, utilizing as less time as possible (max 2s.) for “real time” visualization. should avoid circular shapes and focus on discovery of arbitrary shapes should process a wide range of densities. needs to be statistics friendly (gradual answers rather than binary decisions). needs to evaluate the state of the cluster in relation to a “desired state” (We argue that identifying an emergent formation is significantly influenced by the nature of the observer, their capabilities, knowledge and judgement)

11. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 11 Ognen Paunovski, George Eleftherakis and Tony Cowling PART III The Automated Herd Detection Mechanism

12. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 12 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning for Herd Detection Our Solution: Two-way subjective supervised fuzzy reasoning mechanism for automated detection of herd formations The algorithm incorporates both bottom-up and top-down phases. The observer directly influences the reasoning process through definition of thresholds. Fuzzy reasoning is used in order to express this “blurry” knowledge –The scales of the fuzzy variables are dynamically adjusted according to the simulation parameters

13. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 13 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning algorithm for herd detection Phase 1: Evaluate the preference of an individual to be part of a herd. Phase 2: Identify groups which might form a herd. Phase 3: Evaluate the group coherence in order to find out whether it is a herd or not.

14. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 14 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning about an individual: the bottom-up way Identifying individuals that can possibly be part of a herd: INPUT FUZZY VARIABLES Note: Neighbours are animals which are in the stress + neutral zone –Average distance to neighbours –Number of neighbours OUTPUT FUZZY VARIABLE –Individual’s Herd Belonging Value

15. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 15 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning about an individual: the bottom-up way REASONING RULES –Each rule uses a single fuzzy set for each of the two input variables and associates it with a corresponding fuzzy set in the output variable

16. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 16 Ognen Paunovski, George Eleftherakis and Tony Cowling Transition from bottom-up to top-down Phase I – reasoning about an individual Phase III – reasoning about a group Transition from individual to group Identification of groups of animals Based on the individual HBV a set of “strong” neighbours is identified, these animals form the group skeleton. –Note: “strong” neighbours are animals with high HBV within the stress + neutral zone The group of “strong” neighbours is expanded with all of neighbours of the animals in the group skeleton.

17. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 17 Ognen Paunovski, George Eleftherakis and Tony Cowling Transition from bottom-up to top-down High HBV

18. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 18 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning about a group: the top-down way Identifying whether a group is connected “strongly” enough to form a herd : INPUT FUZZY VARIABLES –Average herd belonging value – denotes the average belonging value for all of the animals in the group. –Herd size – denotes the number of animals in the group. –Spatial area – denotes the size of the spatial area that is occupied by the animals in the group.

19. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 19 Ognen Paunovski, George Eleftherakis and Tony Cowling Reasoning about a group: the top-down way OUTPUT FUZZY VARIABLE –The group herd cohesion value denotes the “strength” of the herd There is a total 96 reasoning rules, applied in the same manner as in the bottom-up way. The final decision whether the herd cohesion value suggest a herd or not is based on a threshold defined by the investigator.

20. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 20 Ognen Paunovski, George Eleftherakis and Tony Cowling Partial screen capture of the herd recognition during model execution. Note: Labels and arrows are added for presentation purpose. CONCLUSION: Evaluation of the detection algorithm showed that the proposed mechanism can be successfully detect herd formations.

21. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 21 Ognen Paunovski, George Eleftherakis and Tony Cowling Evaluation and Identified Problems Evaluation and Testing The testing of the reasoning mechanism was evaluated by comparing the influence of a single input variable on the output variable. Evaluation of the reasoner in respect to the model was done by observation of the reasoner operation during model execution. Result It managed to clearly differentiate between loose groups and herd formations, in most simulation scenarios. It managed to satisfy all of the imposed requirements. –Average comp. time from 0.7 to 1s. for population of 200. Identified Problem An extreme increase or decrease in the population density resulted in malfunction of the reasoner. –Fixed by expressing the scales of the fuzzy variables through density function. Sub-group identification took too much time. –Sub-group identification was excluded from the reasoner, minor effect.

22. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 22 Ognen Paunovski, George Eleftherakis and Tony Cowling PART IV Summary and Future Work

23. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 23 Ognen Paunovski, George Eleftherakis and Tony Cowling Summary and Future work Summary –The algorithm incorporates bottom-up and top-down phases in order to detect herd formations –Our approach incorporates the observer’s view as an important part of the reasoning process –It utilizes fuzzy set theory in order to express a blurry classification criteria. –Differs from fuzzy clustering approaches since we use fuzzy concepts in order to deal with vague clustering criteria rather than expressing fuzzy membership to a cluster –Evaluation showed the approach can be successfully applied in MAS simulation Future Work –Resolution of the identified problems –Execution of the planned experiments

24. Fuzzy Approach to Detection of Emergent Herd Formations in Multi-Agent Simulation 24 Ognen Paunovski, George Eleftherakis and Tony Cowling THANK YOU! Any questions?