MAS-SOC: a Social Simulation Platform Based on Agent-Oriented Programming
Journal of Artificial Societies and Social Simulation
vol. 8, no. 3
<https://www.jasss.org/8/3/7.html>
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Received: 18-Jun-2005 Accepted: 18-Jun-2005 Published: 30-Jun-2005
Abstract Introduction
Overview of MAS-SOC AgentSpeak(L)
 |
| Figure 1. Syntax of AgentSpeak(L). |
in the grammar above) that an
agent is able to perform on its environment. Such actions are also defined as
first-order predicates, but with special predicate symbols (called action
symbols) used to distinguish them from other predicates.
 |
| Figure 2. Examples of AgentSpeak(L) plans. |
'), which in this case ends with the
execution of a plan for choosing the seats for such performance at that
particular venue.
Jason 
) selects a
single event from the set of events; another selection function ( 
) selects an
"option" (i.e., an applicable plan) from a set of applicable plans;
and a third selection function (
) selects one particular
intention from the set of intentions. The selection functions are supposed to
be agent-specific, in the sense that they should make selections based on an
agent's characteristics (though previous work on AgentSpeak(L) did not
elaborate on how designers specify such functions. Therefore, we here leave
the selection functions undefined, however the choices made by them are
supposed to be non-deterministic.
 |
| Figure 3. An Interpretation Cycle of an AgentSpeak Program (Machado 2002). |
has selected an
event, the interpreter has to unify that event with triggering events in the
heads of plans. This generates a set of all relevant plans. By checking
whether the context part of the plans in that set follow from the agent's
beliefs, the interpreter determines a set of applicable plans (plans
that can actually be used at that moment for handling the chosen event). Then
chooses a single applicable plan from that set, which becomes the
intended plan for handling that event, and either pushes that plan on
the top of an existing intention (if the event was an internal one), or
creates a new intention in the set of intentions (if the event was external,
i.e., generated from a perception of the environment).
function selects one of the agent's intentions (i.e., one of the
independent stacks of partially instantiated plans within the set of
intentions). On the top of that stack there is a plan, and the formula in the
beginning of its body is taken for execution. This implies that either a basic
action is performed by the agent on its environment, an internal event is
generated (in the case that the selected formula is an achievement goal), or a
test goal is performed (which means that the set of beliefs has to be
checked).
 |
| Figure 4. Jason IDE. |
 |
| Figure 5. Jason "Mind Inspector" Tool. |
The ELMS Language 
'and `
' signs, so 
, for example, refers to the
position at the upper right diagonal from the agent's current
position. However, the grid definition is optional, as some simulations may
not require any spatial representation. Clearly, there are simulations where
the topology resulting from specific types of agent and object positioning is
the main issue of interest for the investigation for which the simulations are
being used. In contrast, there are also simulations where the existence of a
topology is not relevant at all as, e.g., in a stock market simulation, where
the main issue under consideration relate to the agent interactions
themselves, and perhaps agents' interactions with some types of resources. In
order to make ELMS as general as possible, we chose to make the grid an
optional feature.
The code sample below[4] define an agent-body class named consumer which has five integer attributes, one of them initialised with a random value from 60 to 100. Agents defined in this class are able to execute three types of perception, which will be explained later, and six types of actions. The constructor bellow places the agent in a random cell where the preconditions are true, the cell must not be a shop and may not have a owner already. After the placement, some values are set so that to the agent is defined as the owner of that cell.
<AGENT_BODY NAME="consumer">
<INTEGER NAME = "money"> 1000 </INTEGER>
<INTEGER NAME = "id"> "SELF" </INTEGER>
<INTEGER NAME = "home_x">0 </INTEGER>
<INTEGER NAME = "home_y">0</INTEGER>
<INTEGER NAME = "stamina">
<RANDOM MIN = "60" MAX = "100"/>
</INTEGER>
<PERCEPTIONS>
<ITEM NAME = "see_products"/>
<ITEM NAME = "self_info"/>
<ITEM NAME = "verify_products"/>
</PERCEPTIONS>
<ACTIONS>
<ITEM NAME = "walk_right"/>
<ITEM NAME = "walk_left"/>
<ITEM NAME = "walk_down"/>
<ITEM NAME = "walk_up"/>
<ITEM NAME = "buy"/>
<ITEM NAME = "rest"/>
</ACTIONS>
<CONSTRUCTOR>
<IN_RAND>
<PRECONDITION>
<EQUAL>
<OPERAND>
<CELL_ATT ATTRIBUTE = "shop">
<X>"X"</X>
<Y>"Y"</Y>
</CELL_ATT>
</OPERAND>
<OPERAND> "FALSE" </OPERAND>
</EQUAL>
<EQUAL>
<OPERAND>
<CELL_ATT ATTRIBUTE = "owner">
<X>"X"</X>
<Y>"Y"</Y>
</CELL_ATT>
</OPERAND>
<OPERAND> -1 </OPERAND>
</EQUAL>
</PRECONDITION>
<ELEMENT NAME = "SELFCLASS">
<INDEX>"SELF"</INDEX>
</ELEMENT>
</IN_RAND>
<ASSIGN>
<ELEMENT_ATT NAME = "SELFCLASS" ATTRIBUTE = "home_x" >
<INDEX> "SELF" </INDEX>
</ELEMENT_ATT>
<EXPRESSION> "X"</EXPRESSION>
</ASSIGN>
<ASSIGN>
<ELEMENT_ATT NAME = "SELFCLASS" ATTRIBUTE = "home_y" >
<INDEX> "SELF" </INDEX>
</ELEMENT_ATT>
<EXPRESSION> "Y"</EXPRESSION>
</ASSIGN>
<ASSIGN>
<CELL_ATT ATTRIBUTE = "owner" >
<X>+0</X>
<Y>+0</Y>
</CELL_ATT>
<EXPRESSION> "SELF"</EXPRESSION>
</ASSIGN>
</CONSTRUCTOR>
</AGENT_BODY>
<ACTION NAME="rest">
<PRECONDITION>
<EQUAL>
<OPERATOR>
<CELL_ATT ATTRIBUTE ="owner" >
<X> +0</X>
<Y> +0</Y>
<Z> +0</Z>
</CELL_ATT>
</OPERATOR>
<OPERATOR>
<ELEMENT_ATT NAME = "SELFCLASS" ATTRIBUTE = "id" >
<INDEX> "SELF" </INDEX>
</ELEMENT_ATT>
</OPERATOR>
</EQUAL>
</PRECONDITION>
<ASSIGN>
<ELEMENT_ATT NAME = "consumer" ATTRIBUTE = "stamina" >
<INDEX> "SELF" </INDEX>
</ELEMENT_ATT>
<EXPRESSION>
<RANDOM MIN = "60" MAX = "100"/>
</EXPRESSION>
</ASSIGN>
</ACTION>
In the example above, an action named rest is defined. It has no parameters, but the agent must be on his home cell as defined as a precondition for the action be executed, otherwise the action will fail. If the precondition is satisfied the stamina attribute will receive a random value between 60 and 100 units.
means no owner) and a boolean variable
that keeps the information about whether the cell is a shop or not. In this
example, grid cells do not have reactions.
<DEFGRID SIZEX="10" SIZEY="10" SIZEZ="1">
<BOOLEAN NAME = "shop"> "FALSE" </BOOLEAN>
<INTEGER NAME = "owner"> -1 </INTEGER>
</DEFGRID>
Note that this corresponds to the (default) synchronous simulationmode. An asynchronous mode is also available.
Ontologies
 |
| Figure 6. Top-Ontology. |
 |
| Figure 7. Sample Ontology. |
MAS-SOC Graphical User Interface
 |
| Figure 8. MAS-SOC Manager GUI. |
Example
<RESOURCE NAME="product">
<INTEGER NAME = "id"> "SELF" </INTEGER>
<INTEGER NAME = "type"> 0 </INTEGER>
<INTEGER NAME = "retailer">-1</INTEGER>
<INTEGER NAME = "owner">-1</INTEGER>
<INTEGER NAME = "price"> 0 </INTEGER>
<INTEGER NAME = "quality"> 0 </INTEGER>
<INTEGER NAME = "realquality"> 0 </INTEGER>
<INTEGER NAME = "created"> 0 </INTEGER>
</RESOURCE>
+!refreshBeliefs (NUM)
: product(instance(INS),id(ID)) &
product(instance(INS),realquality(RQ)) &
product(instance(INS),sellquality(SQ)) &
RQ >= SQ
<- ?product(instance(INS),retailer(IDR));
?sellerEvaluation(IDR,Ev);
.addB(Ev,X,NewEv);
-sellerConcept(IDR,Ev);
+sellerConcept(IDR,NewEv).
grid, and 15 consumers
were randomly distributed on the free cells. The shopkeepers were
divided equally, 15 of each type, and there were 5 consumers of each type.
Each consumer had 10 items in the list of items they wanted to buy. The
simulation was run for 200 cycles.
; where 
is the
cycle number when the last purchase was made by the agent; this is done, of
course, for each of the products bought. This means that the agent performance
decreases as the simulation progresses (if no further purchases are made), and
also decreases if the agent has been cheated by the shopkeepers.
. This means that the
shopkeeper's performance is equal to the sum of all money it has
accumulated plus the sum of the cost of the unsold products.
points. Consumers B had the
best performance with an average of 
points (48% more than
consumers A) and consumers C had an average of 
points. This
can be explained by the fact that the consumer A agents have not searched
sufficiently to find good products with good prices. Since they were the first
to buy, they were cheated by the shopkeepers. The consumer B agents,
used the experience of agents A to recognise the cheaters, and had the best
performances. The consumer C agents did too much searching, and when they
decided to buy, another agent might have already bought the products they
wanted to buy, so they had to go after the second or third choice.
money units, while shopkeepers Y had
money units, a difference of almost 26%. The shopkeeper Y agents
(the ones that tried to cheat consumers) had the worse performance, because
they sold only a few products, mostly for the consumer A agents.
Final Remarks Acknowledgements
Notes
2Available at http://jason.sourceforge.net.
3For this reason, a workshop series (held with AAMAS) has recently been created for discussing issues related to multi-agent environments specifically (Weyns 2005).
4Part of the environment definition from the example shown in Section 8.
5Although the list of reactions is the same for all cells, this does not imply they all have the same behaviour at all times, as reactions can have preconditions on the specific state of the individual cells.
6Agents send a message with "true" as its content if they choose to not execute an action in that cycle.
7http://protege.stanford.edu.
8http://www.w3.org/TR/owl-features/.
9In fact, this refers to types of agent, as each of these agents definitions may various instances in a simulation.
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