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1 agent - script ACMEThermostat implements Thermostat in SmartHome {23 cond : Conditioner ;4 therm : Thermometer ;5 threshold : double ;6 condSpeed : double ;78 plan - for Init ( conditioner : Conditioner thermometer : Thermometer ){9 cond = conditioner ; therm = thermometer ; threshold = 1; condSpeed = 0.50;10 }1112 plan - for AchieveTemperature using : cond , therm , log @ mainRoom {13 log ( msg : " achieving temperature "+ targetTemp +" from "+ currentTemp )14 completed - when : java . lang . Math . abs ( targetTemp - currentTemp ) < threshold {15 every - time currentTemp > ( targetTemp + threshold ) && ! isCooling16 => startCooling ( speed : condSpeed )17 every - time currentTemp < ( targetTemp - threshold ) && ! isHeating18 => startHeating ( speed : condSpeed )19 }20 if ( isHeating || isCooling ){21 stop ()22 }23 }2425 plan - for KeepTemperature using : cond , therm , userView {26 quitPlan : boolean = false ;2728 completed - when : quitPlan {29 do - task AchieveTemperature ( targetTemp : desiredTemp )3031 every - time changed desiredTemp => atomically : {32 if (is - doing - any AchieveTemperature ) {33 drop -all - tasks AchieveTemperature34 }35 do - task AchieveTemperature ( targetTemp : desiredTemp )36 }3738 every - time changed currentTemp : !is - doing - any AchieveTemperature39 => do - task AchieveTemperature ( targetTemp : desiredTemp )40 }4142 every - time told newThreshold => {43 threshold = newThreshold44 }4546 every - time changed thermostatStatus : thermStatus . equals == ThermostatStatus . OFF47 => atomically : {48 if ( isCooling || isHeating ){49 stop ()50 }51 drop -all - tasks AchieveTemperature52 quitPlan = true53 }54 }55 }5657 plan - for SelfTest () using : log @ mainRoom , cond {58 log ( msg :" Simple thermostat "+ myName )59 log ( msg :" Using the conditioner " + name in cond )60 }61 }Fig. 3. Definition of a script in simpAL.139
Defining Agent Scripts in simpAL (Fig. 3)The behavior of an agent can be programmed in simpAL through the definition of scripts, that are loaded and executed byagents at runtime. Here we give a very brief account directly by using the ACMEThermostat example Fig. 3. The definitionof an agent script includes the script name, an explicit declaration of the roles played by the script and then the scriptbody, which contains the declaration of a set of beliefs and the definition of a set of plans. Beliefs in simpAL are likesimple variables, characterized by a name, a type and an initial value. The ACMEThermostat script has four beliefs, two tokeep track of the conditioner and thermometer artifacts used to do his job, one for the threshold and one for the speedto be used when using the conditioner. Beliefs declared at the script level are a sort of long-term memory of the agent,useful to keep track information that could be accessed and updated by any plan in execution, and whose lifetime is equalto the one of the agent (script). Plans contain the recipe to execute tasks. The ACMEThermostat script has four plans, toachieve a certain temperature value (line 12-23), to maintain a temperature value (line 25-54), and to do some self test(line 56-59), and also a pre-defined plan to initialize the script, which is executed by default when the script is loaded.To do the AchieveTemperature task, the plan starts cooling or heating – using the conditioner – as soon as the currenttemperature is too high or too low compared to the target one (and the threshold)—the current temperature is observedby the thermometer. The belief about the target temperature (targetTemp) derives from the related attribute (parameter)of the task, while the one about the current temperature (currentTemp) is related to the observable property of the thermthermometer used in the plan. As soon as the current temperature is in the good range, the plan completes—stopping theconditioner if it was working. To do the KeepTemperature task, the plan achieves the desired temperature by immediatelyexecuting the sub-task AchieveTemperature (line 29), which is executed also as soon as the desired temperature changes(lines 31-36) or the current temperature changes and the agent is not already achieving the temperature (line 38-39). Thebelief about the desired temperature (desiredTemp) comes from the observable property of the userView artifact used in theplan. Also, as soon as a message about a new threshold is told by some other agent, the internal value of the threshold isupdated. The plan quits if the agent perceives from the userView artifact that the user has switched off the thermostat.In that case, before quitting the plan, the conditioner is eventually stopped if it was working. Finally, to do the SelfTesttask, the agent simply logs on the console a sequence of information, about his identifier, and the name of the conditionerwhich is using.Some explanations about some key elements of the syntax and semantics of plans follow— a more comprehensive descriptioncan be found here [15] and on simpAL technical documentation. The definition of a plan includes the specification of thetype of task for which the plan can be used and a plan body, which is an action rule block. The action rule block containsthe declaration of a set of local beliefs – that are visible only inside the block, as a kind of short-term memory – and aset of action rules specifying when executing which action. In the simplest case, an action rule is just an action and ablock could be a flat list of actions. In that case, actions are executed in sequence, i.e. every action in the list is executedonly after perceiving the events that the previous one has completed. In the most general case, an action rule is of thekind: every-time | when Event : Condition => Action meaning that the specified action can be executed every time oronce that (when) the specified event occur and the specified condition – which is a boolean expression of the agent beliefsbase – holds. if not specified, the default value of the condition is true. Events concern percepts related to either one of(a) the environment, (b) messages sent by agents, (c) action execution, (d) time passing. All events are actually uniformlymodeled as changes to some belief belonging to agent belief base, given the fact that observable properties, messages sent,action state variables, the internal clock of the agent are all represented as beliefs. Furthermore, the syntax for specifyingevents related to a change of an observable property is changed ObsProp (e.g. line 38), the one for specifying the updateof a belief about an information told by another agent is told What (e.g. line 42). If no event is specified, the pre-definedmeaning is that the rule can be triggered immediately, but only once. Given that, the execution of a flat list of actionscan be obtained by a sequence of action rules with only the action specified. Actions can be either external actions toaffect the environment, i.e. operations provided of some artifact, or communicative actions to directly interact with someother agent (to tell some belief, to assign a task, etc.) or pre-defined internal actions – to update internal beliefs, tomanage tasks in execution, etc. An action can be also an action rule block {...}, which allows then to nest action blocks.Finally, the definition of an action rule block includes the possibility to specify some predefined attributes, for instance: theusing: attribute to specify the list of the identifiers of the artifacts used inside the block (an artifact can be used/observedonly if explicitly declared), the completed-when: to specify the condition for which the action rule block execution can beconsidered completed, the atomically: to specify that the action rule block must be executed as a single action, withoutbeing interrupted or interleaved with blocks of other plans in execution (when the agent is executing multiple tasks at atime).140
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Proceedings of the Tenth Internatio
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OrganisationOrganising CommitteeMeh
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Table of ContentseJason: an impleme
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in Sect. 3 the translation of the J
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init_count(0).max_count(2000).(a)(b
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For instance, a failure in the ERES
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{plan, fun start_count_trigger/1,fu
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single parameter, an Erlang record
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1. Belief annotations. Even though
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decisions taken during the design a
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Conceptual Integration of Agents wi
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Fig. 2. Active component structurep
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the service provider component. As
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Fig. 4. Web Service Invocationretri
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01: public interface IBankingServic
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tate them in the same way as in the
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01: public interface IChartService
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implementations being available for
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deliberative behavior in BDI archit
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layer modules (i.e. nodes) can be d
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different methods to choose the cur
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also a single scheduler module, imp
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andom choice (OR), conditional choi
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- Dealing with conflicts based on p
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5. Brooks, R. A. (1991) Intelligenc
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An Agent-Based Cognitive Robot Arch
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It has been argued that building ro
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EnvironmentHardwareLocal SoftwareC+
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a cognitive layer can connect as a
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can reliably be differentiated and
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4 ExperimentTo evaluate the feasibi
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learn or gain knowledge from experi
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A Programming Framework for Multi-A
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exchange and storage of tuples (key
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Although some success [13] [14] hav
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as well as important non-functional
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component plans have been instantia
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A in the example) can evaluate all
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1. robot-1 issues a Localization(ro
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ACKNOWLEDGMENTThis work has been su
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The code was analysed both objectiv
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Page 99 and 100: principal reasons. Firstly, it is a
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Page 131 and 132: 6 ConclusionWe presented an abstrac
Page 133 and 134: Typing Multi-Agent Programs in simp
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Page 137 and 138: 3.1 simpAL OverviewThe main inspira
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Page 149 and 150: Learning to Improve Agent Behaviour
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Page 165: Author IndexAbdel-Naby, S., 69Alelc
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