In recent work we have extended DACMAS, which is a formalization of ontology-based and commitment-based multi-agent systems. Our extension allows a system to include not only agents but also external contexts. The objective is that of modeling real-world situations where agents not only interact among themselves, but also consult external heterogeneous data- and knowledge-bases to extract useful information. In this paper we further enhance the approach, so that a querying agent is enabled to specify in ontological terms which data it intends to extract from a context, or vice versa needs to be made aware of ontological assumptions the received results are based upon.

1. Exchanging Data
and Ontological Definitions
in Multi-Agent-Contexts Systems
Stefania Costantini Giovanni De Gasperis
Dipartimento di Ingegneria e Scienze dell’Informazione e Matematica
University of L’Aquila, Italy
RuleML 2015, Challenges Track
5 August 2012, Berlin, DE

2. Exchanging Data and Ontological Definitions in Multi-Agent-Contexts
Systems
Abstract
We have extended DACMAS, a formalization of
ontology-based data-aware and commitment-based
multi-agent systems. The extension allows a system to
include not only agents but also external contexts. The
aim is to model real-world situations where agents not
only interact among themselves, but also consult external
heterogeneous data- and knowledge-bases to extract
useful information. In this work we further enhance the
approach, so that a querying agent is enabled to specify in
ontological terms which data it intends to extract from a
context, or vice versa the received results require the
agent to be aware of ontological assumptions they are
based upon.

3. Data Representation and Knowledge
Exchange in Agents
The importance of data/knowledge representation
and exchange in Artificial Intelligence applications is
constantly increasing.
In many application fields it is particularly important
to comprise and elaborate information provided by
multiple sources.
Logic-based data management and exchange are
therefore important issues in logical agents.
Non-monotonic reasoning both in defining and
executing patterns for knowledge exchange and in
the modalities for knowledge exploitation.

4. Tboxes and Aboxes
(Courtesy of http://ai.ia.agh.edu.pl)

5. DACMAS: Data-Aware Commitment-based
Multi-Agent System (MAS)
Introduced by Montali et al, 2014
A recent interesting data-centric agent architecture.
Institutional agent which owns a “global” (DLR-Lite) 1
TBox, specifying the domain in which the MAS
operates.
Each participating agent is equipped with its local
ABox (consistent with the TBox, mutual consistency
not required).
Commitment-based Communication.
(example: buyer agent generating orders became customer, then seller delivers)
Patterns for simple reactive and proactive rules.
1
n-ary version of the DL at the base of the OWL 2 QL profile

6. Our view of DACMAS
DACMAS: yet another agent architecture or useful
Meta-Model for Multi-Agent Systems?
Particularly interesting for a general specification of
data management and communicative features.
Allows for affordable verification (cit. Montali et al).
Very general about an agent program’s definition, so
it can be specialized to many existing agent-oriented
logic languages, including our logic framework DALI.
Missing: access to external knowledge sources.
(this is one of our proposed extension)

7. Accessing External Sources from MAS
Options available:
Agents & Artifacts approach: postulates that a
(homogeneous) description is available for such
sources, that can in general be manipulated by
agents.
(managed) Multi-Context Systems (mMCSs): drop
the assumption of making external sources in some
sense homogeneous: rather, the approach deals
explicitly with their different representation languages
and semantics.
mMCSs: allow for datalog-like non-monotonic queries
to external sources, called “bridge rules”.

8. Our first extension: DACMACS
Data-Aware Commitment-based managed Multi-Agent-Context Systems
Integrates DACMASs and mMCS:
Agents can query (sets of) contexts, but contexts
cannot query agents.
Agents are equipped with bridge rules, whose
application is however activated via special trigger
rules, which allow a bridge rule to be invoked upon
certain conditions and/or according to a certain
timing.
The result of a bridge rule is interpreted as an
agent-generated internal event, and captured by
reactive rules which may determine modifications to
the agent’s ABox.

9. Bridge Rules in mMCSs
how they look like in general form
o(s) ← (c1 : p1), . . . , (cj : pj),
not (cj+1 : pj+1), . . . , not (cm : pm).
where the cis are contexts, i.e., external knowledge
sources, and o is a knowledge integration operator dealt
with by a management function

10. Bridge Rules in a DACMACS
A(ˆx) determinedby E1, . . . , Ek , not Gk+1, . . . , not Gr
where each of the Eis and the Gis are datalog
queries to external context, whose reference is either
locally known or provided by the Institutional agent
via a query Role@inst(role)
A bridge rule is proactively enabled by a trigger rule:
Q(ˆx) enables A(ˆy) [Time | Frequency]
Then, results are exploited via a bridge-update rule:
upon A(ˆx) then β(ˆx)
where β(ˆx) specifies the operator, management
function and actions to be applied to ˆx.

11. Semantics of a DACMACS
Mapped to DALI implementation
DACMAS: operational and asynchronous transition
system, native infrastucture in DALI. 2
mMCSs equilibria : global acceptable data states,
one for each context, guided by bridge rules for
inter-context communication, actuated by means of
operational statements in management functions.
Bridge rules are deemed to be applied whenever
applicable. 3
DACMACS equilibria : they are extended mMCSs
because of the introduction of agents and the bridge
rule applicability implied by agents’ proactive choice.
2
DALI framework supports asynchronous external and internal events with logic programming defined
behaviors of agents
3
DALI precondition rules associated to internal events

12. Properties of a DACMACS
MAS verification
Safe evolution trajectory of a DACMACS: sequence
of equilibria w.r.t. agents’ and contexts’ knowledge
base updates.
Local Consistency: equilibria are composed of
consistent data sets.
A DACMACS (or an mMCS) enjoys Local
Consistency iff all management functions are local
consistency (lc-) preserving.
Global Consistency not required.

13. DACMACS benefits
DACMACS = DACMAS + mMCS =
Data-centric agents + knowledge exchange with
external knowledge bases
Issue: a-priori and run-time verification
Extension: Agents and Contexts exchange
Ontological Definitions
Extension: use acquired ontological definitions for
positive and negative explanations, remember the
sources of such definitions, update their level of trust
and their reputation.

14. Yet an other extension: tractable ontological
query-answering
How to interoperate at ontological level
Agents have their own TBox and can generate
ontological queries to the institutional agent
a global ontology is available to agents and contexts
to achieve interoperability
(example: a shared taxonomy, a dictionary..)
the union of global and local TBoxes is consistent
the local TBox can be updated via bridge rules
the global TBox in a DACMACS is a protected
fragment from agents local TBox updates
new definitions derived from an ontological answer
can be added to the local TBox preserving
consistency

15. Example: Student enrollment
Check for eligibility to enroll
In a DACMACS representing the University, the agent
"student_secretariat" may consult the contexts "student_office"
for grades and "tax_office" for family income.
bridge rule:
eligible(stud) determinedby
low_income(stud) : tax_office,
not nonexcellent(stud) : student_office
low_income(stud) :- student(stud), (1)
family_income(stud, inc), inc ≤ inc_threshold.
nonexcellent(stud) :- student(stud), (2)
grade(stud, grad), grad ≤ grade_threshold.

16. Example: Student enrollment
more general ontology based query
eligible(stud) determinedby
low_income(stud) : tax_office : D1,
not nonexcellent(stud) : student_office : D2
where D1 and D2 are variables initiated by respective
office contexts to (1) and (2)

17. Extended bridge Rules
A(ˆx) determinedby E1, . . . , Ek , not Gk+1, . . . , not Gr
where each of the Eis and the Gis are datalog queries to
external context, whose reference is either locally known
or provided by the Institutional agent via a query with
variables parameters that can be instantiated at context
level
We assume that all variables occurring in A(ˆx) and in each
of the Gis also occur in the Eis.
Within an agent, different bridge rules have distinct
conclusions. The management operations and function
are defined separately.
Satisfiability Preservation: Updates should preserve
satisfiability of basic concepts and roles.
Protection: Updates should preserve the protected
fragment of the TBox.

18. Conclusions
Extended DACMACS allows a system interoperability
between agents and external contexts
external contexts can return not only grounded query
answers, but also their ontological definitions
the overall system is able to evolve by incorporating
new data/knowledge, and new ontological
information, maintaining verifiability at run-time.
the model is currently being mapped to a
DALI/Python based implementation with logical
agents and procedural institutional agent

19. The End!
Thank You for your Attention:-)
Questions?
stefania.costantini@univaq.it
giovanni.degasperis@univaq.it