DALI Multiagent System

Handbook and Examples

How to create and use a DALI Agent: Summary

How the DALI Interpreter starts (provided you have installed Sicstus Prolog)

How to create an agent: the DALI initialization file

How to write a DALI logic program

Architecture of the DALI Interpreter

Examples

How the DALI Interpreter starts

In order to start the DALI Multiagent System it’s necessary to activate the server, next the user module, finally one or several DALI agents.

active_dali.pl/agent.bat

active_dali.pl/agent.bat

active_user.pl/user.bat

active_server.pl/ server.bat

. . .

How the Server starts

We can activate the DALI server either by invoking the the executable file ‘server.bat’ or, via the Sicstus Prolog shell, by loading the file ‘active_server.pl’. This is the command to load the server, that you can fond in the demo directory:

load_files('C:/Programmi/SICStus Prolog 3.11.1/bin/demo/active_server.pl').

active_server.pl/ active_server.exe

DALIDALI

programprogram

file_dali.txt

active_server.pl

active_user.pl

active_dali_wi.pl

How the User Module starts active_user.pl/active_user.exe

We can activate the DALI user module either by using the ‘user.bat' executable file or, via the Sicstus Prolog shell, by loading the file ‘active_user.pl’. This is the command to load the file that you can find in the demo directory:

load_files('C:/Programmi/SICStus Prolog 3.11.1/bin/demo/active_user.pl').

The user module opens a user window for communicating with DALI agents, as explained later on.

The name of the Receiver agent

The language of the message

The list of terms useful to interpretea message

The name of the Sender agent

The content of the message

How the DALI Interpreter starts

We can activate the DALI Interpreter either by using the executable file ‘agent.bat’.

If the paths of the initialization file and of the current installation of Sicstus Prolog set in the batch file are right, the agent will be activated:

.................. Actived Agent ...................

active_dali.pl/agent.bat

DALI logic program

dali dali

programprogram

help.txt

initialization_files

initialization_files

active_server.pl

active_user.pl

active_dali_wi.pl

help.txt

Initialization file

In this way it is possible to activate several DALI agents having different names and logic programs.

DALI initialization file

The initialization file <init>.txt must be in the directory “initialization_dali_files”, and contains the following informations:

The name of the file that contains the DALI logic program; The name of the agent; The adopted ontology ('no' if no ontology is used); The adopted language (Italian,English,…) in the

communication acts; The name of the file containing the tell/told communication

constraints; The name of the communication library; The agent’s abilities ( kind of work, hobbies,…)

These parameters are grouped in a string with prefix ‘agent’, according to the above order, with the syntax exemplified below.

DALI initialization file: example

Example: content of the initialization file

agent('demo/program/italian',gino, '../onto/gino.onto',italian, ['demo/communication'], ['demo/communication_fipa'],[tourist]).

The path is specified starting from the directory where the interpreter is.

DALI initialization file: example

Precisely:

‘gino’ is the name of the agent, ‘italian.onto’ is the ontology file in the directory ‘onto’ and Italian is the language spoken by the agent.

Finally, ‘communication’ is the file ‘communication.con’ in the main directory containing the tell/told constraints and ‘communication_fipa’ is the library with the fipa communication’s primitives.

The last parameter suggests that the agent is a tourist.

At this point, we can write a DALI logic program with .txt extension and put it in the directory program.

In the initialization string (see above) we put the initialization file under thedirectory 'initialization_dali_files' and the DALI logic program file

file_dali.txt’ in the directory program:

Generate a DALI initialization file

DALI DALI

programprogram

file_dali.txt

file_dali.onto

communication_fipa.txt

communication.con

active_dali_wi.pl

ONTO ONTO

How the User Module works active_user.pl/active_user.exe

The user module allows the user to communicate with an existing DALI agents, by means of the DALI primitive send_message(Content,Sender) where Sender=user and Content is an external event we want the agent to perceive. will ask the following arguments:

Insert name of addressee|: pippo. Insert From|: user.

Insert message|: send_message(danger,user).

The name of the Receiver agent

The name of the Sender agent

The content of the message

How to use Ontologies in the User Module(simple way, to be further developed)

If we have for instance, in the DALI logic program, the following reactive rule:

dangerE:>once(ask_for_help).ask_for_help:-call_policeA.call_police:<have_a_phoneP.ask_for_help:-screamA.

If we tell the agent that ‘pericolo’ is equivalent to ‘danger’, we can then send the message send_message(pericolo,user).

Insert name of addressee|: pippo. Insert From|: user.

Insert message|: send_message(pericolo,user).

Write DALI logic program

A Dali program is a file txt whose content is a DALI program.Now we shortly recall the main features of this language by writing a DALI agent:

External events:

The external events are syntactically indicated by the postfix E. When an event enters the agent from its external world, she can perceive it and decide to react. The reaction is defined by a reactive rule which has in its head that external event. The special token :>, used instead of :-, indicates that the reactive rule performs forward reasoning.If we write in the txt file this simple reactive rule:

alarm_clock_ringsE:>stand_upA

the agent observes the following behavior. We use the user module to send to the agent the external event alarm_clock_rings.

Write DALI logic program:External Event

Insert name of addressee|: pippo.Insert From|: user.Insert message|: send_message(alarm_clock_rings,user).

.................. Actived Agent ...................

make(stand_up)

The DALI agent will do the action contained in the body of the reactive rule.

Nome file: alarm.txt

Internal events:

The internal events define a kind of individuality of a DALI agent, making her proactive independently of the environment, of the user and of the other agents, and allowing her to manipulate and revise her knowledge. An internal event is syntactically indicated by the postfix I, and its description is composed of two rules. The first one contains the conditions (knowledge, past events, procedures, etc.) that must be true so that the reaction (in the second rule) may happen.If we write in the txt file those two rules:

i_am_lazy:-alarm_clock_ringsP

not(stand_upP)i_am_lazyI:> i_take_a_vacation_dayA.

the agent exhibits the following behavior. We use the user module to send to the agent the past event alarm_clock_ringsP but this past event could be the past form of the external event as specified in the previous paragraph, and arrived before.

Write DALI logic program:Internal Event

Write DALI logic program:External Event

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(alarm_clock_rings,user).

.................. Actived Agent ...................

make(i_take_a_vacation_day)

The DALI agent will make the action contained in the reaction of the internal event.

Nome file: alarm_clock.txt

Present events:

When an agent perceives an event from the external world it does not necessarily react to it immediately: she has the possibility of reasoning on the event, before (or instead of) triggering a reaction. Reasoning also allows a proactive behavior. In this situation, the event is called present event and is indicated by the suffix N.

arrives_someone:-bell_ringsN.arrives_someoneI:>get_dressedA.get_dressed:< get_undressedP.bell_ringsE:>open_the_doorA.

In this case, when we send the external event bell_rings to the agent, she makes the actions get_dressed and open_the_door if the internal event has been processed before the external event. Else, she does only the action open_the_door. In small DALI programs is unlikely to observe the reaction to the internal event, because the processing of the external events is faster and the interpreter, after the action open_the_door, erases the reaction to the internal event.

Write DALI logic program:Present Event

Write DALI logic program:Present EventsInsert name of addressee|: pippo.Insert From|: user.Insert message|: send_message(bell_rings,user).

.................. Actived Agent ...................

make(open_the_door)make(open_the_door)make(open_the_door)make(open_the_door)make(get_dressed)make(open_the_door)make(open_the_door)make(open_the_door)make(open_the_door)

Nome file: wear.txt

Actions:

Write DALI logic program:Actions

Simple actions: The action in the DALI program is specified as actionA. For example i_go_to_bedA, i_take_the_busA, … When the agent does an action, on the prolog shell you can

observe make(action). Messages as actions: From a DALI program, a message can be sent by writing: messageA(To, Content)

where To is the name of the agent that must receive the communication act and Content is the DALI/FIPA primitive.

Write DALI logic program:Actions

We now describe the primitives used in a DALI program.Sender is the agent that sends the message.

o send_message(External_event, Sender) this primitive is used to call an external event within a reactive

rule; o confirm(Assertion, Sender) this primitive is used to assert a fact within a DALI agent; o disconfirm(Assertion, Sender) this primitive is used to retract a fact within a DALI agent;o propose(Action, List_Conditions,Sender) this primitive is used to propose an agent to do an action

contained within a DALI program. The agent can respond by accepting to do the action (accept_proposal) or by rejecting the proposal (reject_proposal) if the conditions in the message are false. When the agent decides to make the action, she verifies if the conditions of the action rule are true. In this case, the agent does the action or else she sends to the other agent a failure message.

Write DALI logic program:Actions

o execute_proc(Head, Sender) this primitive is used to invoke the head of a generic rule

(procedure) in the DALI program; o query_ref(Fact, N, Sender) this primitive is used to answer an agent on some information

about a not ground Fact. N is the number of the requested matchings;

o agree(Fact, Sender) this primitive is used to answer an agent knowing a ground

Fact;o cancel(Action, Sender) this primitive is used to communicate to an agent to cancel a

requested action. Also in this case, it is difficult to see the effect of this primitive because the agent does immediately the action. To see the effect, the queue of actions must contain several items.

A goal is an objective that an agent must reach. In the DALI language, a goal is a particular internal event that the interpreter begins to attempt when it is invoked. The goal has a postfix G.

Write DALI logic program: Goals

PLANNER

Goal

Environment State

Actions

Plan to reach a goal

...,goal1G,...

goal1:- condition11,...,condition1k

subgoal11G,...,subgoal1NG, subgoalP11,...,subgoalP1N....

goal1:-conditionm1,...,conditionmk

subgoalGm1,...,subgoalGmN, subgoalP11,...,subgoalP1N.

goal1I:>action1,...,goal2G,...,actionk.

How it works:

Write DALI logic program: Goals

We consider a simple example: an agent must wear socks and shoes.

goE:>put_shoesG.put_shoes:-put_right_shoeG,put_left_shoeG,right_shoe_onP,left_shoe_onP.put_shoesI:>left_and_right_shoes_onA,retractall(past(_,_,_)).

put_right_shoe:-put_right_sockG,right_sock_onP.put_right_shoeI:>right_shoe_onA.

put_left_shoe:-put_left_sockG,left_sock_onP.put_left_shoeI:>left_shoe_onA.

put_right_sock:-have_right_sockP.put_right_sockI:>right_sock_onA.

put_left_sock:-have_left_sockP.put_left_sockI:>left_sock_onA.

Write DALI logic program: Goals

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(have_left_sock,user).

.................. Actived Agent ...................

make(right_sock_on)make(right_shoe_on)make(left_sock_on)make(left_shoe_on)make(left_and_right_shoes_on)

Insert name of addressee|: pippo.Insert From|: user.Insert message|:confirm(have_right_sock,user).

Insert name of addressee|: pippo.Insert From|: user.Insert message|: send_message(go,user).

Nome file: shoes.txt

Write DALI logic program: Past events

past(rain,timestamp, user)

is recorded within DALI shell.This past event is called using the string with postfix P. For example, rainP.

Past events:

A past event, indicated by the suffix P, is:

o an external or internal event after a reaction;o an executed action;o a reached goal or subgoal;o a fact communicated using a confirm primitive.

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(rain,user).

ProcessingEventsActionsGoals

Pre-processing file.txt

Communication module

Communication module

The architecture of a DALI agent

The files that the interpreter generates from The files that the interpreter generates from DALI txt fileDALI txt file

file.txt

file.ple

file.plf

file.pl

The DALI interpreter generates some auxiliary files for each agent. These files are put in the same directory as the txt DALI file.

dangerE:>once(ask_for_help).ask_for_help:-call_policeA.call_police:<have_a_phoneP.ask_for_help:-screamA.

remain_at_home:-dangerP,call_policeP.remain_at_homeI:>go_to_bathroomA, close_the_doorA.

go_out:-dangerP,screamP.

go_outI:>go_to_neighbourA.

Reaction rule

External event

Past event

Action

Internal event

Action rule

A simple txt DALI fileA simple txt DALI file

We use this txt file to show how the interpreter works creating ple, plf

and pl files. In order to show the ‘naming’ process we use a new example containing variables.

What the Interpreter records in the ple fileWhat the Interpreter records in the ple file

[danger].

[remain_at_home,go_out,external_refused_action_propose(A,Ag),refused_message(AgM,Con)].

[call_police,scream,go_to_bathroom,close_the_door,go_to_neighbour,message(Ag,inform(query_ref(X,N),values(L),A)),message(Ag,refuse(query_ref(variable),motivation(refused_variables),A)),message(Ag,inform(query_ref(X,N),motivation(no_values),A)),message(Ag,inform(agree(X),values(yes),A)),message(Ag,inform(agree(X),values(no),A))…].

[call_police].

[].

[].

[].

External events

Internal events

Actions

Conditions

Present events

Goals to reach

Goals to test

This file is used by the interpreter to manage the behavior of the agent through the classes of the events, the actions, goals,…

eve(predator_attacks(var_X)):- once(try_to_escape(var_X)).try_to_escape(var_X):- a(fly(var_X)).try_to_escape(var_X):- a(run(var_X)).cd(fly(var_X)):- evp(bird(var_X)), not(evp(abnormal(var_X))).

The ‘naming’ of variables in the pl fileThe ‘naming’ of variables in the pl file

The interpreter generates a pl file where all rules are subjected to ‘naming’ process. All variables has been transformed to costants using the suffix ‘var_’.

predator_attacksE(X):>once(try_to_escape(X)).try_to_escape(X):-flyA(X).try_to_escape(X):-runA(X).fly(X):<birdP(X),not(abnormalP(X)).

Dali txt program

Dali pl program with reified variables

Inte

rpre

ter w

ork

The directives of plf fileThe directives of plf fileWe use this file to set some parameters that determine the behavior of the agent relatively to external, internal, past events and actions .

External event

We can decide if an external event must be processed with a normal or high priority:

external_event(Event,normal).

external_event(Event,high).

Past event

We can decide how long or until which condition a past event mustbe kept in the memory of an agent:

past_event(Event,Seconds). (the past event is kept in memory some Seconds)

past_event(Event,forever).(the past event is kept in memory forever)

past_event(Event,until(Cond)).(the past event is kept in memory until Condition) Action

We can decide if an actionmust be processed with a normal or high priority:

action(Action,normal).

action(Action,high).

Action/Message

We can decide if to submit a message to tell the tell check:

mod(Action, check).

The directives of plf fileThe directives of plf fileNow we examine the directives on internal events.

Internal event

We can set several parameters for an internal event in order to tune the Interpreter behaviour:

o the frequency(seconds) with which the interpreter attempts the internal event;

o how many times the agent must react if the internal event is true (1,2,..,forever);

o when the agent must react: this parameter is ‘true’ if the reaction must happen forever or else we can link the reaction to some past events belonging to body of the first rule of the internal event; when the past event inside a ‘change list’ is modified the agent will be able to react again. For example, if we have the internal event: think:-rainP, go_outP,buy_umbrellaP. thinkI:>open_the_umbrellaA.

we can set parameters as: internal_event(think,3,1,change([rain,go_out]),forever).

o when/until when the interpreter must attempt the internal event: o until_date(Date): till certain date;o until_cond(Condition): when the Condition is false;o forever: forever

Some directives of plf example fileSome directives of plf example file

action(call_police,normal).action(scream,normal).action(go_to_bathroom,normal).action(close_the_door,normal).action(go_to_neighbour,normal).external_event(danger,normal).

past_event(danger,20).past_event(remain_at_home,20).past_event(go_out,20).past_event(call_police,20).past_event(scream,20).past_event(go_to_bathroom,20).past_event(close_the_door,20).past_event(go_to_neighbour,20).

internal_event(remain_at_home,3,forever,true,until_cond(past(remain_at_home))).internal_event(go_out,3,forever,true,until_cond(past(go_out))).

mod(message(_,inform(_,motivation(refused_message),_)),check).

The action is put in the queue with normal priority

This past event is kept in memory 20 seconds

This internal event is attempted every 3 seconds

The agent will react forever

We specify no conditions

This internal event is attempted until the past event remain_at_home becomes true

This message is submitted to tell check

The Communication architectureThe Communication architecture

META LEVEL

DALI INTERNAL INTERPRETER

TELL CHECK

TOLD CHECK

Incoming message

Outcoming message

The message passes this level only if the corresponding told rule is true.

If the agent doesn’t know the content of the message, she calls the meta-level and uses the ontology and/or other properties in order to understand the communication act.

The message, submitted to tell check, is sent only if the corresponding tell rule is true.

The Told Check levelThe Told Check level

TOLD CHECK

Incoming message

Each DALI agent has a ‘con’ file, specified in the initialization file, that contains the told/tell rules. These rules, external to interpreter, can be modified by the user. The structure of a told rule is:

told(Sender,Content):-constraint1,…,constraintn.

In this example, an agent receives a message, using the primitive send_message, only if the Sender agent isn’t an enemy:

told(Ag,send_message(_)):-not(enemyP(Ag)).

A message that does not go through the told level is eliminated. The agent Sender receives an inform message asserted as a past event.

The Meta-LevelThe Meta-Level

Each DALI agent uses an (optional) meta procedure written in the ‘communication.con’ file that specifies how the entity can interprete an unknown message. This procedure can bemodified by the user. In the initialization file the user can specify the ontology file that contains informations about the ontology that the agent must use. This file contains informations about the location of the ontology ( remember that ontologies are used by means of Sesame semantic repositories ).

"PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>". ← Prefixes of namespaces"/openrdf-sesame/repositories/Ontology". ← Location of the ontology"localhost:8080". ← Location of Tomcat ( On which runs SESAME ).

META LEVEL

The Tell Check levelThe Tell Check level

The user can set the mod string in the plf file in order to submit a message to tell check.In this case, the message is actually sent only if the corresponding tell rule is true.The tell rules can be modified by the user. The structure of a tell rule is:

tell(Receiver,Sender,Content):-constraint1,…,constraintn.

In this example, an agent sends a message, using the primitive send_message, only if she has a trust greater than 4 in the Receiver agent:

tell(To,_,send_message(_)):-trustP(_,To,N),N>4.

A message that does not go through the tell level is eliminated.

TELL CHECK

Outcoming message

Examples – A dangerous situation…

One Agent!

Examples – A dangerous situation…

Initialization file name: help.txtDALI logic file name: help.txt

DALI DALI

programprogram

help.txt

active_server.pl

active_user.pl

active_dali.pl

help.txt initalization_files

initalization_files

Examples – A dangerous situation…

dangerE:>once(ask_for_help).ask_for_help:-call_policeA.call_police:<have_a_phoneP.ask_for_help:-screamA.

remain_at_home:-dangerP,call_policeP.remain_at_homeI:>go_to_bathroomA, close_the_doorA.

go_out:-dangerP,screamP.go_outI:>go_to_neighbourA.

DALI logic program help.txt

Examples – A dangerous situation…

Run the DALI logic program

.................. Actived Agent ...................

make(scream)make(go_to_neighbour)

Insert name of addressee|: pippo.Insert From|: user.Insert message|: send_message(danger,user).

Examples – A dangerous situation…

If the agent has a phone…

.................. Actived Agent ...................

make(call_police)make(go_to_bathroom)make(close_the_door)

New messageInsert name of addressee|: pippo. Insert From|: user. Insert message|: confirm(have_a_phone,user).New messageInsert name of addressee|: pippo. Insert From|: user. Insert message|: send_message(danger,user).

she reacts differently!

Two Agents!

Examples – Error Recovery planning…

Examples – Error Recovery planning…

Initialization file name: recovery.txt and sensor.txtDALI logic file name: recovery.txt and sensor.txt

DALI DALI

programprogram

recovery.txt

active_server.pl

active_user.pl

active_dali.pl

recovery.txt

sensor.txt

sensor.txt

initalization_files

initalization_files

Examples – Error Recovery planning…

DALI logic program recovery.txt

error_recovery(M,M1):-go_rightP(_,M),goal(M),informP(reality(M,M1),sensor),M\=M1.

error_recovery(M,M1):-go_leftP(_,M),goal(M),informP(reality(M,M1),sensor),M\=M1.

error_recovery(M,M1):-go_forwardP(_,M),goal(M),informP(reality(M,M1),sensor),M\=M1.

error_recoveryI(M,M1):>recovery_errorA(M,M1),drop_pastA(reality(M,M1)).

reached_goal(M):-go_rightP(_,M),goal(M).

reached_goal(M):-go_leftP(_,M),goal(M).

reached_goal(M):-go_forwardP(_,M),goal(M).

reached_goalI(M):>clause(agent(Ag),_),

messageA(sensor,send_message(what_about_my_position(M,Ag),Ag)).

…

right(exit1,bank1).

right(exit2,bank2).

left(bank1,hospital1).

forward(bank2,bank1).

goal(hospital1).

Examples – Error Recovery planning…

DALI logic program sensor.txt

what_about_my_positionE(M,Ag):>once(examine_position(M,Ag,_)).

examine_position(M,Ag,M1):-messageA(Ag,inform(reality(M,M1),sensor)),wrong_positionA(M,Ag).message(_,inform(reality(M,M1),sensor)):<errorP(M,M1).

examine_position(M,Ag,_):-right_positionA(M,Ag).

Error Recovery planning

STATION

Exit2Exit1

Bank2 Bank1

HospitalThe environment…

Error Recovery planning

STATION

Exit2Exit1

Bank2 Bank1

HospitalFirst situation: the agent exits from Exit1 and goes to Hospital1

confirm(i_am_at_exit(exit1),user)

make(go_right(exit1,bank1))make(go_left(bank1,hospital1))

Run the DALI logic programs recovery.txt and sensor.txt

.................. Actived Agent ...................make(go_right(exit1,bank1))make(go_left(bank1,hospital1))send_message_to(sensor,send_message(what_about_my_position(hospital1,pippo),pippo),italian,[])

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(i_am_at_exit(exit1),user).

Examples – Error Recovery planning…

.................. Actived Agent ...................make(right_position(hospital1,pippo))

robot pippo

sensor

Error Recovery planning

STATION

Exit2Exit1

Bank2 Bank1

Hospital

make(go_right(exit2,bank2))make(go_forward(bank2,bank1))make(go_left(bank1,hospital1))

Second situation: the agent exits from Exit2 and goes to Hospital1

confirm(i_am_at_exit(exit2),user)

Run the DALI logic programs recovery.txt and sensor.txt

.................. Actived Agent ...................make(go_right(exit2,bank2))make(go_forward(bank2,bank1))make(go_left(bank1,hospital1))send_message_to(sensor,send_message(what_about_my_position(hospital1,pippo),pippo),italian,[])

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(i_am_at_exit(exit2),user).

Examples – Error Recovery planning…

.................. Actived Agent ...................make(right_position(hospital1,pippo))

robot pippo

sensor

Error Recovery planning

STATION

Exit2Exit1

Bank2 Bank1

Hospital1

Third situation: the agent thinks to be at Exit1 while really she is at Exit2. When she is at Hospital1/Bank2

a planner recognizes the error and modifies the plan.

make(go_right(exit1,bank1))make(go_left(bank1,hospital1))make(recovery_error(hospital1,bank2))make(go_forward(bank2,bank1))make(go_left(bank1,hospital1))

?Hospital1

Run the DALI logic programs recovery.txt and sensor.txt

.................. Actived Agent ...................

Insert name of addressee|: sensor.Insert From|: user.Insert message|: confirm(error(hospital1,bank2),user).

Examples – Error Recovery planning… (step1)

sensor

Run the DALI logic programs recovery.txt and sensor.txt

Insert name of addressee|: sensor.Insert From|: user.Insert message|: confirm(i_am_at_exit(exit1),user).

Examples – Error Recovery planning… (step2)

.................. Actived Agent...............send_message_to(pippo,inform(reality(hospital1,bank2),sensor),italian,[])make(wrong_position(hospital1,pippo))

sensor

.................. Actived Agent ...................make(go_right(exit1,bank1))make(go_left(bank1,hospital1))send_message_to(sensor,send_message(what_about_my_position(hospital1,pippo),pippo),italian,[])make(recovery_error(hospital1,bank2))make(go_forward(bank2,bank1))make(go_left(bank1,hospital1))

robot pippo

Two Agents!

Examples – Informations and Meta-reasoning…

Initialization file name: agent1.txt and agent2.txtDALI logic file name: agent1.txt and agent2.txt

DALI DALI

programprogram

agent2.txt

initialization_files

initialization_files

active_server.pl

active_user.pl

active_dali_wi.pl

agent2.txt

agent1.txt

agent1.txt

Examples – Informations and Meta-reasoning…

DALI logic program agent1.txt

rainE:-open_the_umbrellaA.

Examples – Informations and Meta-reasoning…

DALI logic program agent2.txt

i_am_illE:>go_to_family_doctorA.

We will consider two simple agents. We are interested in showing how the communication primitives query_ref and agree use the meta-level reasoning.

Examples – Informations and Meta-reasoning…

We will communicate to agent1 the fact ama(james,julia). In the ontology that the agent ‘pippo’ adopts there is the fact:

ontology(pippo,ama,love).

Run the DALI logic program agent1.txt

Insert name of addressee|: pippo.Insert From|: user.Insert message|: confirm(ama(james,julia),user).

Examples – Informations and Meta-reasoning…

Agent2 will ask agent1 about the james/julia love. The agent2 ‘pino’ will use the communication primitives query_ref and agree.

Run the DALI logic program agent2.txt

Insert the path and the name of the initialization file:|: 'demo/agent2.txt'.

New messageInsert name of addressee|: pippo. Insert From|: pino. Insert message|: query_ref(ama(julia,Y),1,pino).

.................. Actived Agent ...................send_message_to(pino,inform(query_ref(ama(julia,fdvar_9),1),values([ama(james,julia)]),pippo),italian,[]) (fdvar is the reification method of the Sicstus Prolog)

Examples – Informations and Meta-reasoning…

New messageInsert name of addressee|: pippo. Insert From|: pino. Insert message|: query_ref(love(julia,Y),1,pino).

.................. Actived Agent ...................send_message_to(pino,inform(query_ref(love(julia,fdvar_10),1),values([ama(james,julia)]),pippo),italian,[])

Examples – Informations and Meta-reasoning…

New messageInsert name of addressee|: pippo. Insert From|: pino. Insert message|: agree(love(julia,james),pino).

.................. Actived Agent ...................send_message_to(pino,inform(agree(love(julia,james)),values(yes),pippo),italian,[])

Three Agents!

Examples – Cooperation and Ontology…

Initialization file name: italian.txt, english.txt and translator.txtDALI logic file name: italian.txt, english.txt and translator.txt

Examples – Cooperation and Ontology…

demo demo

program

program english.txt

initialization_files initialization_files

active_server.pl

active_user.pl

active_dali_wi.pl

english.txt

italian.txt

translator.txt

translator.txt

italian.txt

DALI logic program italian.txt

questionE(Q,L,M):>agente(A,_,_,_), once(examine_question(Q,L,M,A)).

examine_question(Q,_,M,A):-messageA(M,send_message(know(Q,A),A)).message(_,send_message(know(Q,A),A)):<clause(know(Q,A),_);knowP(Q,A).

examine_question(Q,L,M,A):-messageA(translator,send_message(translate(Q,L,M,A),A)).message(translator,send_message(translate(_,L,_,_),_)):< L=english.

examine_question(Q,L,M,A):-messageA(M,send_message(not_know(Q,L,A),A)).message(_,send_message(not_know(_,L,_),_)):< L=italian.

…

know(io,gino).know(amo,gino).know(odio,gino).

Examples – Cooperation and Ontology…

ITALIAN

DALI logic program italian.txt

questionE(Q,L,M):>agente(A,_,_,_), once(examine_question(Q,L,M,A)).

examine_question(Q,_,M,A):-messageA(M,send_message(know(Q,A),A)).message(_,send_message(know(Q,A),A)):<clause(know(Q,A),_);knowP(Q,A).

examine_question(Q,L,M,A):-messageA(translator,send_message(translate(Q,L,M,A),A)).message(translator,send_message(translate(_,L,_,_),_)):< L=english.

…

know(io,gino).know(amo,gino).know(odio,gino).

Examples – Cooperation and Ontology…

ITALIAN

DALI logic program english.txt

questionE(Q,L,M):>agente(A,_,_,_), once(examine_question(Q,L,M,A)).

examine_question(Q,_,M,A):-messageA(M,send_message(know(Q,A),A)).message(_,send_message(know(Q,A),A)):<clause(know(Q,A),_);knowP(Q,A).

examine_question(Q,L,M,A):-messageA(translator,send_message(translate(Q,L,M,A),A)).message(translator,send_message(translate(_,L,_,_),_)):< L=italian.

…

know(i,susy).know(love,susy).know(hate,susy).know(you,susy).

Examples – Cooperation and Ontology…

DALI logic program translator.txt

translateE(Q,L,M,A):>once(examine_translation(Q,L,M,A)).examine_translation(Q,_,M,A):-clause(translated(Q,Tr),_), messageA(A,send_message(question(Tr,italian,M),translator)).

examine_translation(Q,_,M,A):-translatedP(Q,Tr), messageA(A,send_message(question(Tr,italian,M),translator)).

examine_translation(Q,_,M,A):-clause(translated(Tr,Q),_), messageA(A,send_message(question(Tr,english,M),translator)).

…

translated(i,io).translated(love,amo).translated(hate,odio).translated(you,te).

Examples – Cooperation and Ontology…

ITALIAN

TRANSLATOR

Cooperation and Ontology

ITALIAN ENGLISH

questionE(Term,Language,From)

know(i,susy).know(love,susy).know(hate,susy).know(you,susy).

know(io,gino).know(amo,gino).know(odio,gino).know(te,gino).

translated(i,io).translated(love,amo).translated(hate,odio).translated(you,te).

The environment…

ITALIAN

Cooperation and Ontology

ITALIAN ENGLISH

know(io,_)

know(amo,_)

know(te,_)

know(i,_)

know(love,_)

know(you,_)

evviva_loves_me(susy)

Run the DALI logic programs italian.txt

Examples – Cooperation and Ontology…

Insert name of addressee|: gino.Insert From|: susy.Insert message|: send_message(question(i,english,susy),susy).

Insert name of addressee|: gino.Insert From|: susy.Insert message|: send_message(question(love,english,susy),susy).

Insert name of addressee|: gino..Insert From|: susy.Insert message|: send_message(question(you,english,susy),susy).

.................. Actived Agent...............make(comprehension)make(comprehension)make(comprehension)

english/susy

.................. Actived Agent ...................send_message_to(gino,send_message(question(io,italian,susy),translator),italian,[])send_message_to(gino,send_message(question(amo,italian,susy),translator),italian,[])send_message_to(gino,send_message(question(te,italian,susy),translator),italian,[])

Examples – Cooperation and Ontology…

.................. Actived Agent...............send_message_to(translator,send_message(translate(i,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(io,gino),gino),italian,[])send_message_to(translator,send_message(translate(love,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(amo,gino),gino),italian,[])send_message_to(translator,send_message(translate(you,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(te,gino),gino),italian,[])make(evviva_loves_me(susy))

italian/gino

translator

ITALIAN

Cooperation and Ontology

ITALIAN ENGLISH

know(io,_)know(odio,_)know(te,_)

know(i,_)know(hate,_)know(you,_)

i_will_not_speak_with(susy)

add_pastA(enemy(susy))

told(Ag,send_message(_)):-not(enemyP(Ag)).

know(you,_)Eliminated message

Examples – Cooperation and Ontology…

Insert name of addressee|: gino.Insert From|: susy.Insert message|: send_message(question(i,english,susy),susy).

Insert name of addressee|: gino.Insert From|: susy.Insert message|: send_message(question(hate,english,susy),susy).

Insert name of addressee|: gino.Insert From|: susy.Insert message|: send_message(question(you,english,susy),susy).

........ Actived Agent........make(comprehension)make(comprehension)make(comprehension)make(comprehension)

english/susy

............. Actived Agent ..........send_message_to(gino,send_message(question(te,italian,susy),translator),italian,[]) …

Examples – Cooperation and Ontology…

.................. Actived Agent...............send_message_to(translator,send_message(translate(you,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(te,gino),gino),italian,[])send_message_to(translator,send_message(translate(i,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(io,gino),gino),italian,[])send_message_to(translator,send_message(translate(hate,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(odio,gino),gino),italian,[])make(i_will_not_speak_with(susy))Eliminated message:conditions not verified for send_message(question(hate,english,susy),susy)From:susy:arianna:1075Language:italianOntology:[]send_message_to(susy,inform(send_message(question(hate,english,susy),susy),motivation(refused_message),gino),italian,[])

italian/gino

translator

ITALIAN

ITALIANENGLISH

Cooperation and Ontology

send_message(give_present(A),A)

(the filter accepts the messages as give_present(_))

accept_presentA,drop_pastA(enemy(A))

Susy,after the present, isn’t an enemy and the communication is again authorized by the filter.

Examples – Cooperation and Ontology…

.................. Actived Agent...............make(accept_present)send_message_to(translator,send_message(translate(hate,english,susy,gino),gino),italian,[])send_message_to(susy,send_message(know(odio,gino),gino),italian,[])

recovery_friendship(Ag):-informP(_,motivation(refused_message),Ag).recovery_friendshipI(Ag):>clause(agent(A),_), messageA(Ag,send_message(give_present(A),A)).

italian/gino

.................. Actived Agent...............send_message_to(gino,send_message(give_present(susy),susy),italian,[])make(comprehension)

english/susy