scml.scml2020.awi

Implements the Agent-World-Interface for SCML2020 worlds

Classes

AWI

The Agent SCML2020World Interface for SCML2020 world.

Module Contents

class scml.scml2020.awi.AWI(world: negmas.situated.world.World, agent: negmas.situated.agent.Agent)[source]

Bases: negmas.AgentWorldInterface

The Agent SCML2020World Interface for SCML2020 world.

This class contains all the methods needed to access the simulation to extract information which are divided into 5 groups:

Static World Information:

Information about the world and the agent that does not change over time. These include:

  1. Market Information:

  • n_products: Number of products in the production chain.

  • n_processes: Number of processes in the production chain.

  • n_competitors: Number of other factories on the same production level.

  • all_suppliers: A list of all suppliers by product.

  • all_consumers: A list of all consumers by product.

  • catalog_prices: A list of the catalog prices (by product).

  • inputs: Inputs to every manufacturing process.

  • outputs: Outputs to every manufacturing process.

  • is_system: Is the given system ID corresponding to a system agent?

  • is_bankrupt: Is the given agent bankrupt (None asks about self)?

  • current_step: Current simulation step (inherited from negmas.situated.AgentWorldInterface ).

  • n_steps: Number of simulation steps (inherited from negmas.situated.AgentWorldInterface ).

  • relative_time: fraction of the simulation completed (inherited from negmas.situated.AgentWorldInterface).

  • settings: The system settings (inherited from negmas.situated.AgentWorldInterface ).

  1. Agent Information:

  • profile: Gives the agent profile including its production cost, number of production lines, input product index, mean of its delivery penalties, mean of its disposal costs, standard deviation of its shortfall penalties and standard deviation of its disposal costs. See OneShotProfile for full description. This information is private information and no other agent knows it.

  • n_lines: the number of production lines in the factory (private information).

  • is_first_level: Is the agent in the first production level (i.e. it is an input agent that buys the raw material).

  • is_last_level: Is the agent in the last production level (i.e. it is an output agent that sells the final product).

  • is_middle_level: Is the agent neither a first level nor a last level agent

  • my_input_product: The input product to the factory controlled by the agent.

  • my_output_product: The output product from the factory controlled by the agent.

  • my_input_products: All input products of a factory controlled by the agent. Currently, it is always a list of one item. For future compatibility.

  • my_output_products: All output products of a factory controlled by the agent. Currently, it is always a list of one item. For future compatibility.

  • available_for_production: Returns the line-step slots available for production.

  • level: The production level which is numerically the same as the input product.

  • my_suppliers: A list of IDs for all suppliers to the agent (i.e. agents that can sell the input product of the agent).

  • my_consumers: A list of IDs for all consumers to the agent (i.e. agents that can buy the output product of the agent).

  • penalties_scale: The scale at which to calculate disposal cost/delivery penalties. “trading” and “catalog” mean trading and catalog prices. “unit” means the contract’s unit price while “none” means that disposal cost/shortfall penalty are absolute.

  • n_input_negotiations: Number of negotiations with suppliers.

  • n_output_negotiations: Number of negotiations with consumers.

  • state: The full state of the agent ( FactoryState ).

  • current_balance: The current balance of the agent

  • current_inventory: The current inventory of the agent (quantity per product)

Dynamic World Information:

Information about the world and the agent that changes over time.

  1. Market Information:

  • trading_prices: The trading prices of all products. This information is only available if publish_trading_prices is set in the world.

  • exogenous_contract_summary: A list of n_products tuples each giving the total quantity and average price of exogenous contracts for a product. This information is only available if publish_exogenous_summary is set in the world.

  1. Other Agents’ Information:

  • reports_of_agent: Gives all past financial reports of a given agent. See FinancialReport for details.

  • reports_at_step: Gives all reports of all agents at a given step. See FinancialReport for details.

  1. Current Negotiations Information:

  • current_input_issues: The current issues for all negotiations to buy the input product of the agent. If the agent is at level zero, this will be empty.

  • current_output_issues: The current issues for all negotiations to buy the output product of the agent. If the agent is at level n_products - 1, this will be empty.

  1. Agent Information:

  • spot_market_quantity: The quantity the agent bought from the spot market at

    a given step

  • spot_market_loss: The spot market loss for the agent.

Actions:

  1. Negotiation Control:

  • request_negotiations: Requests a set of negotiations controlled by a single controller.

  • request_negotiation: Requests a negotiation controlled by a single negotiator.

  1. Production Control:

  • schedule_production: Schedules production using one of the predefined scheduling strategies.

  • order_production: Orders production directly for the current step.

  • set_commands: Sets production commands directly on the factory.

  • cancel_production: Cancels a scheduled production command.

Services (All inherited from negmas.situated.AgentWorldInterface):

  • logdebug/loginfo/logwarning/logerror: Logs to the world log at the given log level.

  • logdebug_agent/loginf_agnet/…: Logs to the agent specific log at the given log level.

  • bb_query: Queries the bulletin-board.

  • bb_read: Read a section of the bulletin-board.

request_negotiations(is_buy: bool, product: int, quantity: int | Tuple[int, int], unit_price: int | Tuple[int, int], time: int | Tuple[int, int], controller: negmas.SAOController | None = None, negotiators: List[negmas.Negotiator] = None, partners: List[str] = None, extra: Dict[str, Any] = None, copy_partner_id=True) → bool[source]

Requests a negotiation

Parameters:

  • is_buy – If True the negotiation is about buying otherwise selling.

  • product – The product to negotiate about

  • quantity – The minimum and maximum quantities. Passing a single value q is equivalent to passing (q,q)

  • unit_price – The minimum and maximum unit prices. Passing a single value u is equivalent to passing (u,u)

  • time – The minimum and maximum delivery step. Passing a single value t is equivalent to passing (t,t)

  • controller – The controller to manage the complete set of negotiations

  • negotiators – An optional list of negotiators to use for negotiating with the given partners (in the same order).

  • partners – ID of all the partners to negotiate with.

  • extra – Extra information accessible through the negotiation annotation to the caller

  • copy_partner_id – If true, the partner ID will be copied to the negotiator ID

Returns:

True if the partner accepted and the negotiation is ready to start

Remarks:

  • You can either use controller or negotiators. One of them must be None.

  • All negotiations will use the following issues in order: quantity, time, unit_price

  • Negotiations with bankrupt agents or on invalid products (see next point) will be automatically rejected

  • Valid products for a factory are the following (any other products are not valid):

    1. Buying an input product (i.e. product $in$ my_input_products ) and an output product if the world settings allows it (see allow_buying_output)

    1. Selling an output product (i.e. product $in$ my_output_products ) and an input product if the world settings allows it (see allow_selling_input)

request_negotiation(is_buy: bool, product: int, quantity: int | Tuple[int, int], unit_price: int | Tuple[int, int], time: int | Tuple[int, int], partner: str, negotiator: negmas.SAONegotiator, extra: Dict[str, Any] = None) → bool[source]

Requests a negotiation

Parameters:

  • is_buy – If True the negotiation is about buying otherwise selling.

  • product – The product to negotiate about

  • quantity – The minimum and maximum quantities. Passing a single value q is equivalent to passing (q,q)

  • unit_price – The minimum and maximum unit prices. Passing a single value u is equivalent to passing (u,u)

  • time – The minimum and maximum delivery step. Passing a single value t is equivalent to passing (t,t)

  • partner – ID of the partner to negotiate with.

  • negotiator – The negotiator to use for this negotiation (if the partner accepted to negotiate)

  • extra – Extra information accessible through the negotiation annotation to the caller

Returns:

True if the partner accepted and the negotiation is ready to start

Remarks:

  • All negotiations will use the following issues in order: quantity, time, unit_price

  • Negotiations with bankrupt agents or on invalid products (see next point) will be automatically rejected

  • Valid products for a factory are the following (any other products are not valid):

    1. Buying an input product (i.e. product $in$ my_input_products ) and an output product if the world settings allows it (see allow_buying_output)

    1. Selling an output product (i.e. product $in$ my_output_products ) and an input product if the world settings allows it (see allow_selling_input)

schedule_production(process: int, repeats: int, step: int | Tuple[int, int] = ANY_STEP, line: int = ANY_LINE, override: bool = True, method: str = 'latest', partial_ok: bool = False) → Tuple[numpy.ndarray, numpy.ndarray][source]

Orders the factory to run the given process at the given line at the given step

Parameters:

  • process – The process to run

  • repeats – How many times to repeat the process

  • step – The simulation step or a range of steps. The special value ANY_STEP gives the factory the freedom to schedule production at any step in the present or future.

  • line – The production line. The special value ANY_LINE gives the factory the freedom to use any line

  • override – Whether to override existing production commands or not

  • method – When to schedule the command if step was set to a range. Options are latest, earliest

  • partial_ok – If true, allows partial scheduling

Returns:

Tuple[int, int] giving the steps and lines at which production is scheduled.

Remarks:

  • The step cannot be in the past. Production can only be ordered for current and future steps

  • ordering production of process -1 is equivalent of cancel_production only if both step and line are given

order_production(process: int, steps: numpy.ndarray, lines: numpy.ndarray) → None[source]

Orders production of the given process

Parameters:

  • process – The process to run

  • steps – The time steps to run the process at as an np.ndarray

  • lines – The corresponding lines to run the process at

Remarks:

  • len(steps) must equal len(lines)

  • No checks are done in this function. It is expected to be used after calling available_for_production

available_for_production(repeats: int, step: int | Tuple[int, int] = ANY_STEP, line: int = ANY_LINE, override: bool = True, method: str = 'latest') → Tuple[numpy.ndarray, numpy.ndarray][source]

Finds available times and lines for scheduling production.

Parameters:

  • repeats – How many times to repeat the process

  • step – The simulation step or a range of steps. The special value ANY_STEP gives the factory the freedom to schedule production at any step in the present or future.

  • line – The production line. The special value ANY_LINE gives the factory the freedom to use any line

  • override – Whether to override any existing commands at that line at that time.

  • method – When to schedule the command if step was set to a range. Options are latest, earliest, all

Returns:

Tuple[np.ndarray, np.ndarray] The steps and lines at which production is scheduled.

Remarks:

  • You cannot order production in the past or in the current step

  • Ordering production, will automatically update inventory and balance for all simulation steps assuming that this production will be carried out. At the indicated step if production was not possible (due to insufficient funds or insufficient inventory of the input product), the predictions for the future will be corrected.

set_commands(commands: numpy.ndarray, step: int = -1) → None[source]

Sets the production commands for all lines in the given step

Parameters:

  • commands – n_lines vector of commands. A command is either a process number to run or NO_COMMAND to keep the line idle

  • step – The step to set the commands at. If < 0, it means current step

cancel_production(step: int, line: int) → bool[source]

Cancels any production commands on that line at this step

Parameters:

  • step – The step to cancel production at (must be in the future).

  • line – The production line

Returns:

success/failure

Remarks:

  • The step cannot be in the past or the current step. Cancellation can only be ordered for future steps

property trading_prices: numpy.ndarray[source]

Returns the current trading prices of all products

property exogenous_contract_summary: List[Tuple[int, int]][source]

The exogenous contracts in the current step for all products

Returns:

A list of tuples giving the total quantity and total price of all revealed exogenous contracts of all products at the current step.

property allow_zero_quantity: bool[source]

Does negotiations allow zero quantity?

property state: scml.scml2020.common.FactoryState[source]

Receives the factory state

property current_balance[source]

Current balance of the agent

property current_inventory[source]

Current inventory of the agent

reports_of_agent(aid: str) → Dict[int, scml.scml2020.common.FinancialReport][source]

Returns a dictionary mapping time-steps to financial reports of the given agent

reports_at_step(step: int) → Dict[str, scml.scml2020.common.FinancialReport][source]

Returns a dictionary mapping agent ID to its financial report for the given time-step

property profile: scml.scml2020.common.FactoryProfile[source]

Gets the profile (static private information) associated with the agent

property all_suppliers: List[List[str]][source]

Returns a list of agent IDs for all suppliers for every product

property all_consumers: List[List[str]][source]

Returns a list of agent IDs for all consumers for every product

property inputs: numpy.ndarray[source]

Returns the number of inputs to every production process

property outputs: numpy.ndarray[source]

Returns the number of outputs to every production process

property n_competitors: int[source]

Returns the number of factories/agents in the same production level

property my_input_product: int[source]

Returns a list of products that are inputs to at least one process the agent can run

property my_output_product: int[source]

Returns a list of products that are outputs to at least one process the agent can run

property my_input_products: numpy.ndarray[source]

Returns a list of products that are inputs to at least one process the agent can run

property my_output_products: numpy.ndarray[source]

Returns a list of products that are outputs to at least one process the agent can run

property my_suppliers: List[str][source]

Returns a list of IDs for all of the agent’s suppliers (agents that can supply at least one product it may need).

Remarks:

  • If the agent have multiple input products, suppliers of a specific product $p$ can be found using: self.all_suppliers[p].

property my_consumers: List[str][source]

Returns a list of IDs for all the agent’s consumers (agents that can consume at least one product it may produce).

Remarks:

  • If the agent have multiple output products, consumers of a specific product $p$ can be found using: self.all_consumers[p].

property n_lines: int[source]

The number of lines in the corresponding factory. You can read state to get this among other information

property catalog_prices: numpy.ndarray[source]

Returns the catalog prices of all products

property n_products: int[source]

Number of products in the world

property n_processes: int[source]

Returns the number of processes in the system

property is_first_level[source]

Whether this agent is in the first production level

property is_last_level[source]

Whether this agent is in the last production level

property level[source]

The production level which is the index of the process for this factory (or the index of its input product)

property is_middle_level[source]

Whether this agent is in neither in the first nor in the last level

is_system(aid: str) → bool[source]

Checks whether an agent is a system agent or not

Parameters:

aid – Agent ID

is_bankrupt(aid: str | None = None) → bool[source]

Checks whether the agent is bankrupt

Parameters:

aid – Agent ID (None means self)

spot_market_quantity(step: int | None) → int[source]

The quantity bought by the agent from the spot market at the given step.

Parameters:

step – The simulation step (day)

Remarks:

If step is None, the current step will be used

spot_market_loss(step: int | None) → int[source]

The spot market loss of the agent at the given step.

Parameters:

step – The simulation step (day)

Remarks:

If step is None, the current step will be used