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Modelling and analysis of collective adaptive systems Michele Loreti quancol . ........ . . . ... ... ... ... ... ... ... Based on joint work with Yehia Abd Alrahman, Rocco De Nicola, Jane Hillston Annual Meeting of IFIP Working

  1. Interaction patterns in CAS Typically, CAS exhibit two kinds of interaction pattern: 1 Spreading : one agent spreads relevant information to a given group of other agents 2 Collecting : one agent changes its behaviour according to data collected from one agent belonging to a given group of agents. Spreading: 1-to-many Collecting: 1-to-1 Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 10 / 34

  2. Interaction patterns in CAS Typically, CAS exhibit two kinds of interaction pattern: 1 Spreading : one agent spreads relevant information to a given group of other agents 2 Collecting : one agent changes its behaviour according to data collected from one agent belonging to a given group of agents. Spreading: 1-to-many Collecting: 1-to-1 Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 10 / 34

  3. Interaction patterns in CAS Typically, CAS exhibit two kinds of interaction pattern: 1 Spreading : one agent spreads relevant information to a given group of other agents 2 Collecting : one agent changes its behaviour according to data collected from one agent belonging to a given group of agents. Spreading: 1-to-many Collecting: 1-to-1 Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 10 / 34

  4. Interaction primitives for CAS The following interaction primitives, all based on attribute oriented communication , can be considered for CAS: Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 11 / 34

  5. Interaction primitives for CAS The following interaction primitives, all based on attribute oriented communication , can be considered for CAS: Broadcast output : a message is sent to all the components satisfying a predicate π ; Broadcast input : a process is willing to receive a broadcast message from a component satisfying a predicate π ; Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 11 / 34

  6. Interaction primitives for CAS The following interaction primitives, all based on attribute oriented communication , can be considered for CAS: Broadcast output : a message is sent to all the components satisfying a predicate π ; Broadcast input : a process is willing to receive a broadcast message from a component satisfying a predicate π ; Unicast output : a message is sent to one of the components satisfying a predicate π ; Unicast input : a process is willing to receive a message from a component satisfying a predicate π . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 11 / 34

  7. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  8. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  9. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 The global knowledge of the system and that of its agents; 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  10. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 The global knowledge of the system and that of its agents; 3 The environment where agents operate. . . 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  11. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 The global knowledge of the system and that of its agents; 3 The environment where agents operate. . . taking into account open ended-ness and adaptation; 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  12. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 The global knowledge of the system and that of its agents; 3 The environment where agents operate. . . taking into account open ended-ness and adaptation; taking into account resources, locations and visibility/reachability issues. 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  13. Carma : a process specification language for CAS To support design of CAS we introduced Carma 2 ( Collective Adaptive Resource-sharing Markovian Agents ). This is a process specification language which handles: 1 The behaviours of agents and their interactions; 2 The global knowledge of the system and that of its agents; 3 The environment where agents operate. . . taking into account open ended-ness and adaptation; taking into account resources, locations and visibility/reachability issues. In Carma the execution of an action takes an exponentially distributed time; the rate of each action is determined by the environment. 2 Michele Loreti and Jane Hillston. “Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools”. In: SFM 2016 . Vol. 9700. Lecture Notes in Computer Science. Springer, 2016, pp. 83–119. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 12 / 34

  14. CAS: Carma perspective Collective Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 13 / 34

  15. CAS: Carma perspective Collective Environment Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 13 / 34

  16. CAS: Carma perspective Collective Environment Attributes Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 13 / 34

  17. CARMA A Carma system consists of Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 14 / 34

  18. CARMA A Carma system consists of a collective ( N ). . . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 14 / 34

  19. CARMA A Carma system consists of a collective ( N ). . . . . . operating in an environment ( E ). Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 14 / 34

  20. CARMA A Carma system consists of a collective ( N ). . . . . . operating in an environment ( E ). Collective. . . is composed by a set of components, i.e. the Markovian agents that compete and/or cooperate to achieve a set of given tasks models the behavioural part of a system Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 14 / 34

  21. CARMA A Carma system consists of a collective ( N ). . . . . . operating in an environment ( E ). Collective. . . is composed by a set of components, i.e. the Markovian agents that compete and/or cooperate to achieve a set of given tasks models the behavioural part of a system Environment. . . models the rules intrinsic to the context where agents operate; mediates and regulates agent interactions. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 14 / 34

  22. Components Agents in Carma are defined as components C of the form ( P , γ ) where. . . P is a process, representing agent behaviour; γ is a store, modelling agent knowledge. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 15 / 34

  23. Components Agents in Carma are defined as components C of the form ( P , γ ) where. . . P is a process, representing agent behaviour; γ is a store, modelling agent knowledge. Process Syntax: P , Q ::= nil | act . P | P + Q | P | Q | [ π ] P | kill | A Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 15 / 34

  24. Components Agents in Carma are defined as components C of the form ( P , γ ) where. . . P is a process, representing agent behaviour; γ is a store, modelling agent knowledge. Process Syntax: P , Q ::= nil | act . P | P + Q | P | Q | [ π ] P | kill | A Store γ is a mapping from attribute names to values . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 15 / 34

  25. Interaction primitives Syntax α ⋆ [ π ] �− → act ::= e � σ Broadcast output α ⋆ [ π ]( − → | x ) σ Broadcast input α [ π ] �− → | e � σ Unicast output α [ π ]( − → | x ) σ Unicast input α is an action type; π is a predicate; σ is the effect of the action on the store. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 16 / 34

  26. Updating the store After the execution of an action, a process can update the component store: σ denotes a function mapping each γ to a probability distribution over possible stores. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 17 / 34

  27. Updating the store After the execution of an action, a process can update the component store: σ denotes a function mapping each γ to a probability distribution over possible stores. move ⋆ [ π ] � v �{ x := x + U ( − 1 , +1) } Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 17 / 34

  28. Updating the store After the execution of an action, a process can update the component store: σ denotes a function mapping each γ to a probability distribution over possible stores. move ⋆ [ π ] � v �{ x := x + U ( − 1 , +1) } Remark: Processes running in the same component can implicitly interact via the local store; Updates are instantaneous. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 17 / 34

  29. More on synchronisation Predicates regulating broadcast/unicast inputs can refer also to the received values. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 18 / 34

  30. More on synchronisation Predicates regulating broadcast/unicast inputs can refer also to the received values. Example: A value greater than 0 is expected from a component with a trust level less than 3: α ⋆ [( x > 0) ∧ ( trust level < 3)]( x ) σ . P Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 18 / 34

  31. More on synchronisation Predicates regulating broadcast/unicast inputs can refer also to the received values. Example: A value greater than 0 is expected from a component with a trust level less than 3: α ⋆ [( x > 0) ∧ ( trust level < 3)]( x ) σ . P Pattern matching can be encoded in Carma . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 18 / 34

  32. Examples of interactions. . . Broadcast synchronisation: ( stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � ( stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � ( stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 19 / 34

  33. Examples of interactions. . . Broadcast synchronisation: ( stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � ( stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � ( stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 19 / 34

  34. Examples of interactions. . . Broadcast synchronisation: ( stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � ( stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � ( stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 19 / 34

  35. Examples of interactions. . . Broadcast synchronisation: ( stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � ( stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � ( stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) ⇓ ( P , σ 1 ( { role = “ master ” } )) � ( Q 1 [ v / x ] , σ 2 ( { bl = 4% } )) � (stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( Q 3 [ v / x ] , σ 4 ( { bl = 2% } )) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 19 / 34

  36. Examples of interactions. . . Broadcast synchronisation: (stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � (stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 45% } ) � (stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 25% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 20 / 34

  37. Examples of interactions. . . Broadcast synchronisation: (stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � (stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 45% } ) � (stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 25% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 20 / 34

  38. Examples of interactions. . . Broadcast synchronisation: (stop ⋆ [bl < 5%] � v � σ 1 . P , { role = “ master ” } ) � (stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 45% } ) � (stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 25% } ) ⇓ ( P , σ 1 ( { role = “ master ” } )) � (stop ⋆ [role = “ master ”]( x ) σ 2 . Q 1 , { bl = 45% } ) � (stop ⋆ [role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop ⋆ [ ⊤ ]( x ) σ 4 . Q 3 , { bl = 25% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 20 / 34

  39. Examples of interactions. . . Unicast synchronisation: (stop[bl < 5%] �•� σ 1 . P , { role = “ master ” } ) � (stop[role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � (stop[role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop[ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 21 / 34

  40. Examples of interactions. . . Unicast synchronisation: (stop[bl < 5%] �•� σ 1 . P , { role = “ master ” } ) � (stop[role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � (stop[role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop[ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 21 / 34

  41. Examples of interactions. . . Unicast synchronisation: (stop[bl < 5%] �•� σ 1 . P , { role = “ master ” } ) � (stop[role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � (stop[role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop[ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 21 / 34

  42. Examples of interactions. . . Unicast synchronisation: (stop[bl < 5%] �•� σ 1 . P , { role = “ master ” } ) � (stop[role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � (stop[role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � (stop[ ⊤ ]( x ) σ 4 . Q 3 , { bl = 2% } ) ⇓ ( P , σ 1 ( { role = “ master ” } )) � (stop[role = “ master ”]( x ) σ 2 . Q 1 , { bl = 4% } ) � (stop[role = “ super ”]( x ) σ 3 . Q 2 , { bl = 2% } ) � ( Q 3 , σ 4 ( { bl = 2% } )) Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 21 / 34

  43. Modelling the environment Interactions between components can be affected by the environment: a wall can inhibit wireless interactions; two components are too distant to interact; . . . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 22 / 34

  44. Modelling the environment Interactions between components can be affected by the environment: a wall can inhibit wireless interactions; two components are too distant to interact; . . . The environment. . . is used to model the intrinsic rules that govern the physical context; Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 22 / 34

  45. Modelling the environment Interactions between components can be affected by the environment: a wall can inhibit wireless interactions; two components are too distant to interact; . . . The environment. . . is used to model the intrinsic rules that govern the physical context; consists of a pair ( γ , ρ ): Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 22 / 34

  46. Modelling the environment Interactions between components can be affected by the environment: a wall can inhibit wireless interactions; two components are too distant to interact; . . . The environment. . . is used to model the intrinsic rules that govern the physical context; consists of a pair ( γ , ρ ): a global store γ , that models the overall state of the system; Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 22 / 34

  47. Modelling the environment Interactions between components can be affected by the environment: a wall can inhibit wireless interactions; two components are too distant to interact; . . . The environment. . . is used to model the intrinsic rules that govern the physical context; consists of a pair ( γ , ρ ): a global store γ , that models the overall state of the system; an evolution rule ρ that regulates component interactions (receiving probabilities, action rates,. . . ). Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 22 / 34

  48. The evolution rule It is assumed that all actions in Carma take some time complete and that this duration is governed by an exponential distribution. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 23 / 34

  49. The evolution rule It is assumed that all actions in Carma take some time complete and that this duration is governed by an exponential distribution. However the action descriptions do not include any information about the timing (unlike many other stochastic process algebras). Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 23 / 34

  50. The evolution rule It is assumed that all actions in Carma take some time complete and that this duration is governed by an exponential distribution. However the action descriptions do not include any information about the timing (unlike many other stochastic process algebras). We also do not assume perfect communication, i.e. there may be a probability that an interaction will fail to complete even between components with appropriately match attributes. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 23 / 34

  51. The evolution rule It is assumed that all actions in Carma take some time complete and that this duration is governed by an exponential distribution. However the action descriptions do not include any information about the timing (unlike many other stochastic process algebras). We also do not assume perfect communication, i.e. there may be a probability that an interaction will fail to complete even between components with appropriately match attributes. The environment manages these aspects of system behaviour, and others in the evolution rule. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 23 / 34

  52. The evolution rule ρ ρ is a function, dependent on current time, the global store and the current state of the collective, returns a tuple of functions ε = � µ p , µ w , µ r , µ u � known as the evaluation context µ p ( γ s , γ r , α ): the probability that a component with store γ r can receive a broadcast message α from a component with store γ s ; µ w ( γ s , γ r , α ): the weight to be used to compute the probability that a component with store γ r can receive a unicast message α from a component with store γ s ; µ r ( γ s , α ) computes the execution rate of action α executed at a component with store γ s ; µ u ( γ s , α ) determines the updates on the environment (global store and collective) induced by the execution of action α at a component with store γ s . Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 24 / 34

  53. Carma Operational Semantics The operational semantics of Carma specifications is defined in terms of three functions that compute the possible next states of a component, a collective and a system: Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 25 / 34

  54. Carma Operational Semantics The operational semantics of Carma specifications is defined in terms of three functions that compute the possible next states of a component, a collective and a system: 1 the function C that describes the behaviour of a single component; Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 25 / 34

  55. Carma Operational Semantics The operational semantics of Carma specifications is defined in terms of three functions that compute the possible next states of a component, a collective and a system: 1 the function C that describes the behaviour of a single component; 2 the function N ε builds on C to describe the behaviour of collectives; Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 25 / 34

  56. Carma Operational Semantics The operational semantics of Carma specifications is defined in terms of three functions that compute the possible next states of a component, a collective and a system: 1 the function C that describes the behaviour of a single component; 2 the function N ε builds on C to describe the behaviour of collectives; 3 the function S t that shows how Carma systems evolve. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 25 / 34

  57. Quantitative Analysis The semantics of carma gives rise to a Continuous Time Markov Chain (CTMC). This can be analysed by by numerical analysis of the CTMC for small systems; by stochastic simulation of the CTMC; by fluid approximation of the CTMC under certain restrictions (particularly on the environment). Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 26 / 34

  58. A running example. . . Bike Sharing System. . . We want to use Carma to model a bike sharing system where: bikes are made available in a number of stations that are placed in various areas of a city; Users that plan to use a bike for a short trip can pick up a bike at a suitable origin station return it to any other station close to their planned destination. we assume that the city is partitioned in homogeneous zones. . . and that all the stations in the same zone can be equivalently used by any user in that zone. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 27 / 34

  59. CaSL : Component Prototypes The BSS scenario. . . Two kinds of components, one for each of the two groups of agents involved in our BSS, can be considered: parking stations; users. PS attributes: User attributes: zone : indicates where the zone : current user location; station is located; dest : user destination. capacity : the number of slots installed in the station; available : the number of available bikes. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 28 / 34

  60. The Carma Eclipse Plug-in http://quanticol.sourceforge.net/ Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 29 / 34

  61. CaSL : BSS Analysis Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 30 / 34

  62. CaSL : BSS Analysis In this scenario the use of stations is not well balanced! Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 30 / 34

  63. CaSL : an alternative model for BSS To overcome this problem we can consider a model where stations located at the same zone do not compete but cooperate. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 31 / 34

  64. CaSL : an alternative model for BSS To overcome this problem we can consider a model where stations located at the same zone do not compete but cooperate. We consider a variant of stations that, when located at the same zone, interact to avoid unbalanced use of resources. Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 31 / 34

  65. CaSL : an alternative model for BSS To overcome this problem we can consider a model where stations located at the same zone do not compete but cooperate. We consider a variant of stations that, when located at the same zone, interact to avoid unbalanced use of resources. Each station can use broadcast to advertise other agents about the use of resources! Michele Loreti (UniFi) Modelling and analysis of CAS IFIP WG 2.2 31 / 34

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