=Paper=
{{Paper
|id=Vol-487/paper-13
|storemode=property
|title=Computational Logic and Agent Based Systems
|pdfUrl=https://ceur-ws.org/Vol-487/paper13.pdf
|volume=Vol-487
|dblpUrl=https://dblp.org/rec/conf/birthday/MancarellaT08
}}
==Computational Logic and Agent Based Systems==
https://ceur-ws.org/Vol-487/paper13.pdf
Il Milione: A Journey in the Computational Logic in Italy
La Logica Computazionale in sistemi basati su agenti
Computational Logic in Agent Based Systems
Paolo Mancarella e Francesca Toni
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Il Milione: A Journey in the Computational Logic in Italy
SOMMARIO/ABSTRACT briefly summarize one such approach which has lead to the
defnition of the KGP model for agency, and which is being
Viene descritto l’utilizzo della logica computazionale further developed to cope with the specification of agents
a supporto della formalizzazione ed implementazione in service-oriented applications.
di agenti in sistemi multi-agente. In questo ambito è
necessario l’uso di varie forme di logica computazionale,
tra le quali abduzione, argomentazione e sistemi basati 2 The KGP model
su preferenze. Viene presentato a grandi linee il modello
KGP is an acronym for Knowledge, Goals and Plan. The
per agenti denominato KGP, nonché una sua estensione
model is intended to provide a modular and hierarchical
in corso di definizione per la modellazione di agenti
specification of agents equipped with a variety of advanced
in ambienti distribuiti quali il Grid e più in generale
reasoning features to allow intelligent decision making and
architetture service-oriented
behaviour. KGP agents are particularly suited to open dy-
namic environments where they have to adapt to changes
We describe recent work on the deployment of computa-
in their environment and they have to function in circum-
tional logic to support the formalisation and implemen-
stances where they have incomplete information. Here we
tation of agents in multi-agent systems. Several forms
give an overview of the KGP agent model and its compo-
of computational logic systems are needed in this setting,
nents [4, 3]. The model relies upon
including abductive, argumentative and preference-based
− an internal (or mental) state, holding the agent
systems. We briefly sketch the agent model called KGP,
Knowledge base (beliefs), Goals (desires) and Plans (in-
and an ongoing extension of it which is needed to model
tentions),
agents in distributed settings such as the Grid and, more
− a set of reasoning capabilities,
generally, Service-oriented architectures.
− a set of physical capabilities,
− a set of transition rules, defining how the state of the
Keywords: Logica computazionale, sistemi multi-agente,
agent changes, and defined in terms of the above capabili-
abduzione, argomentazione
ties,
− a set of selection operators, to enable and provide ap-
1 Introduction propriate inputs to the transitions,
− a cycle theory, providing the control for deciding which
Computational Logic (CL) has been successfully adopted, transitions should be applied when, and defined using the
in recent years, in modelling agents within agent based sys- selection operators. The model is defined in a modular
tems. The adoption of CL techniques has the advantage fashion, in that different activities are encapsulated within
that formal specifications come along with their compu- different capabilities and transitions, and the control is a
tational counterparts in the form of provably correct and separate module. The model also has a hierarchical struc-
executable proof procedures. The formal and computa- ture, depicted in figure 1.
tional models needed in the agent based settings require
Internal state. This is a tuple hKB0 , F, C, Σi, where:
an integrated treatment of different features, which can be
handled within various extensions of the basic logic pro- • KB0 holds the (dynamic) beliefs of the agent about
gramming framework, including abduction, argumentation the external world in which it is situated, as well as a
and constraint logic programming. In this short paper we record of the actions it has already executed.
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Il Milione: A Journey in the Computational Logic in Italy
strained sub-goals and actions designed for achieving
the input goals.
• Reactivity, which is used to provide new reactive top-
level goals, as a reaction to perceived changes in the
environment and the current plans held by the agent.
• Goal Decision, which is used to revise the non-
reactive top-level goals, adapting the agent’s state to
changes in its own preferences and in the environ-
ment.
• Identification of Preconditions and Identification of
Effects for actions, which are used to determine ap-
propriate sensing actions for checking whether ac-
Figure 1: A graphical overview of the KGP model tions may be safely executed (if their preconditions
are known to hold) and whether recently executed ac-
tions have been successful (by checking that some of
• F is a forest of trees whose nodes are goals, which their known effects hold).
may be executable or not. Each tree in the forest
gives a hierachical presentation of goals, in that the • Temporal Reasoning, which allows the agent to rea-
tree represents the construction of a plan for the root son about the evolving environment, and to make pre-
of the tree. The set of leaves of any tree in F forms dictions about properties, including non-executable
a currently chosen plan for achieving the root of the goals, holding in the environment, based on the (par-
tree. Executable goals are actions which may be tial) information the agent acquires over its life-time.
physical, communicative, or sensing. Non-executable
• Constraint Solving, which allows the agent to reason
goals may be mental or sensing. Only non-executable
about the satisfiability of the temporal constraints in
mental goals may have children, forming (partial)
C and Σ.
plans for them. Actions have no children in any trees
in F. The roots of trees in F are referred to as
In the concrete realisation of the KGP model, we have
top-level goals, the executable goals are referred to
chosen to realise the above capabilities in various exten-
as actions, and non-executable goals which are not
sions of the logic programming paradigm. In particular,
top-level goals are referred to as sub-goals. Top-
we use (conventional) logic programming for Identifica-
level goals are classified as reactive or non-reactive.
tion of Preconditions and Effects, abductive logic program-
Roughly speaking, reactive goals are generated in re-
ming with constraints for Planning, Reactivity and Tempo-
sponse to observations, e.g. communications received
ral Reasoning, and logic programming with priorities for
from other agents and changes in the environment, for
Goal Decision.
example to repair plans that have already been gener-
ated. Non-reactive goals, on the other hand, are the Physical capabilities. In addition to the reasoning capa-
chosen desires of the agent. Note that some top-level bilities, the agent is equipped with “physical” capabilities,
(reactive) goals may be actions. linking the agent to its environment, consisting of
• C is the Temporal Constraint Store, namely a set of • A Sensing capability, allowing the agent to observe
constraint atoms in some given underlying constraint that properties hold or do not hold, and that other
language. These basically constrain the time variables agents have executed actions.
of the goals in F. For example, they may specify a
time window over which the time of an action can be • An Actuating capability, for executing (physical and
instantiated, at execution time. communicative) actions.
• Σ is a set of equalities instantiating time variables Transitions. The state hKB0 , F, C, Σi of an agent
with time constants. For example, when the time vari- evolves by applying transition rules, which employ the ca-
ables of actions are instantiated at action execution pabilities as follows:
time, records of the instantiations are kept in Σ.
• Goal Introduction (GI), possibly changing the top-
Reasoning capabilities. These are: level goals in F, and using Goal Decision.
• Planning, which generates plans for mental goals • Plan Introduction (PI), possibly changing F and C
given as input. These plans consist of temporally con- and using Planning.
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Il Milione: A Journey in the Computational Logic in Italy
• Reactivity (RE), possibly changing the reactive top- within a single framework of abductive logic program-
level goals in F and possibly C, and using the Reac- ming, temporal reasoning, constraint logic programming,
tivity capability. and preference reasoning based on argumentation in order
to support a diverse collection of capabilities. Each one of
• Sensing Introduction (SI), possibly introducing new these is specified declaratively and equipped with its own
sensing actions in F for checking the preconditions provably correct computational counterpart. These com-
of actions already in F. putational models are heavily based upon proof procedures
• Passive Observation Introduction (POI), changing for (various extensions of) logic programming. In particu-
KB0 by recording unsolicited information coming lar, the operational model for KGP agents relies upon CIFF
from the environment, and using Sensing. [2], a proof procedure for abductive logic programming
with constraints, and Gorgias [1], for logic programming
• Active Observation Introduction (AOI), possibly with priorities. These procedures have been obtained by
changing Σ and KB0 , by recording the outcome of adapting and suitably extending two existing proof proce-
(actively sought) sensing actions, and using Sensing. dures for logic programming, namely Fung and Kowalskis
IFF procedure for abductive logic programming for CIFF,
• Action Execution (AE), executing all types of actions and Kakas and Tonis argumentation-based procedure for
and as a consequence changing KB0 and Σ, and us- negation as failure in logic programming for Gorgias. The
ing Actuating. overall operational models are sound and (in some cases)
complete with respect to the abstract KGP model, and form
• State Revision (SR), possibly revising F, and using
a solid bridge between the KGP model and its implemen-
Temporal Reasoning and Constraint Solving.
tation within the PROSOCS platform, a prototype imple-
mentation using Sicstus Prolog and JXTA [5].
Cycle and Selection operators. The behaviour of an
agent is given by the application of transitions in se-
quences, repeatedly changing the state of the agent. These 3 Argumentative agents in ARGUGRID
sequences are not determined by fixed cycles of behaviour,
as in conventional agent architectures, but rather by reason- The use of agent technology offers a solution to dynamic
ing with cycle theories. Cycle theories define preference service composition in distributed settings such as the Grid
policies over the order of application of transitions, which and more generally Service-Oriented Architectures. Dif-
may depend on the environment and the internal state of ferent services can be associated with autonomous agents
an agent. They rely upon the use of selection operators for that can identify and negotiate, on behalf of service re-
detecting which transitions are enabled and what their in- questors and providers, implementation plans that take into
puts should be, as follows: account the requirements of both sides. The ARGUGRID
−action selection for inputs to AE; project1 aims at defining and deploying argumentation-
this selection operator uses the Temporal Reasoning and based agents to support the selection and composition of
Constraint Solving capabilities; − goal selection for inputs services over the Grid and Service-Oriented Architectures
to PI; [6]. We have proposed in [7] an agent architecture inte-
this selection operator uses the Temporal Reasoning and grating a number of argumentative modules (for various
Constraint Solving capabilities; − effect selection for in- forms of decision-making), a module for interaction with
puts to AOI; this selection operator uses the Identification other agents, “physical” modules for carrying out this in-
of Effect reasoning capability; teraction via communication, and several data structures.
− precondition selection for inputs to SI; this selection op- This type of argumentative agents can be seen as a vari-
erator uses the Identification of Preconditions, Temporal ant of KGP agents. This variant relies upon the use of an
Reasoning and Constraint Solving capabilities. argumentation decision-making tool supporting all reason-
The provision of a declarative control for agents in the ing capabilities, and it makes use of argumentative proto-
form of cycle theories is a highly novel feature of the cols for persuasion in negotiation. Argumentative agents
model, which could, in principle, be imported into other are equipped with a specialised internal state, consisting of
agent systems. In the concrete realisation of the KGP requirements, abstract or partially instantiated workflows,
model, we have chosen to realise cycle theories in the same concrete workflows, planned communicative actions and
framework of logic programming with priorities and con- actions executed in the past by the agent or by others, and
straints that we also use for Goal Decision. arguments. The KGP modular architecture allows us to
adopt a specialised set of reasoning capabilities supporting
2.1 Computational model the various forms of decision-making needed in ARGU-
GRID and inter-agent interaction, as well as a capability
One central distinguishing feature of the KGP model, in for revising the knowledge/beliefs of agents, which is actu-
comparison with other models for agency, including those
based on logic programming, is its modular integration 1 www.argugrid.eu
73
Il Milione: A Journey in the Computational Logic in Italy
ally missing in the original KGP model. Specialised phys- 4 Conclusions
ical capabilities are also needed in this setting to provide
suitable forms of inter-agent communications, and finally We have briefly described work carried out in recent years
appropriate transitions encapsulate the new capabilities. In and still ongoing, which aims at adopting computational
this setting, agents need to be able to perform communica- logic for the description of agents in agent based systems.
tive actions (for requesting services, accepting or refusing The use of computational logic allows us, on one hand to
the provision of services, etc.) and actions for consulting partially fill the gap between agent models and their com-
registries, inquiring about services and their providers. In putational realization. Indeed, the specification of KGP
their internal state, agents store (a selection of) all commu- agent is a sort of executable specification due to the fact
nicative acts they have participated in, as either speakers or that the computational logic tools adopted in this setting
receivers, as well as the set of their current commitments, are equipped with suitable concrete proof procedures. On
namely the contracts they have committed to. Basically, teh other hand, the modularity of the KGP model allows
argumentative KGP agent are characterised by one to extend it naturally to support new forms of reason-
ing, such as the ones needed in order to model the type
• a (transient) state, consisting of of agents needed in service-oriented applications. Again,
− a knowledge base, called KB0 as for the KGP computational logic tools, based on various forms of argu-
model, but holding communicative acts by or to the mentation, can be adopted in these settings to support the
agent, acts for consulting registries by the agent, as new type of capabilities needed, such as decision making
well as contracts and negotiation. This is still ongoing work we are carrying
− a set of goals, namely requirements by the user out within the ARGUGRID project.
“owning” the agent
− a set of decisions, of different kinds (to get ser-
vices of known types from some yet-to-be-decided REFERENCES
providers or from some known providers, or a deci- [1] N. Demetriou and A. C. Kakas. Argumentation with
sion to utter something, or a decision to consult some abduction. In Proceedings of the fourth Panhellenic
registry) Symposium on Logic, 2003.
− a set of arguments, providing justifications and rea-
sons for goals and decisions in the state [2] U. Endriss, P. Mancarella, F. Sadri, G. Terreni, and
F. Toni. The CIFF proof procedure for abductive logic
• a number of extended reasoning capabilities, namely
programming with constraints. Lecture Notes in Arti-
abstract decision-making, social decision-making,
ficial Intelligence, 3229:680–684, 2004.
communicative reactivity, registry consultation; each
capability is supported by an appropriate argumenta- [3] A. C. Kakas, P. Mancarella, F. Sadri, K. Stathis, and
tion system (base) F. Toni. Declarative agent control. Lecture Notes in
• a revision capability, for modifying the argumentation Artificial Intelligence, 3487:96–110, 2005.
systems supporting the various reasoning capabilities [4] A.C. Kakas, P. Mancarella, F. Sadri, K. Stathis, and
• physical capabilities, namely listening, talking, and F. Toni. The KGP model of agency. In R. Lopez de
consulting Mantaras and L. Saitta, editors, Proceedings of the Six-
teenth European Conference on Artificial Intelligence,
• a set of transitions, namely ADM (using the abstract Valencia, Spain (ECAI 2004). IOS Press, August 2004.
decision-making capability), SDM (using the social
decision-making capability), CR (using the commu- [5] K. Stathis, A.C. Kakas, W. Lu, N. Demetriou, U. En-
nicative reactivity capability), RC (using the registry driss, and A. Bracciali. PROSOCS: A platform for
consultation capability), R (using the revision capa- programming software agents in computational logic.
bility), LI, TA, CON (using the listening, talking and Applied Artificial Intelligence, 20(4-5), 2006.
consulting capabilities, respectively)
[6] F. Toni. Argumentative KGP agents for service com-
• a control, in the form of a conditional policy, that, for position. Proc. AITA08, Architectures for Intelli-
each transition, gives one or more possible next tran- gent Theory-Based Agents, AAAI Spring Symposium,
sitions depending on whether a number of conditions March 2008, Stanford University, CA, USA, 2008.
hold or not.
[7] M. Morge, P. Mancarella, F. Toni, J. McGinnis, S. Bro-
Here, the consulting capability is intended for accessing
muri, and K. Stathis Toward a modular architecture of
information in registries. The reasoning capabilities corre-
argumentative agents to compose services. In Proceed-
spond to the IDM (individual decision-making), SDM (so-
ings JFSMA 2007, 2007.
cial decision making), and SI (social interaction) modules
in [7]. The listening and talking capabilities are special
cases of sensing and actuating in the KGP model.
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Il Milione: A Journey in the Computational Logic in Italy
5 Contacts
Paolo Mancarella (Corresponding Author)
Dipartimento di Informatica, Università di Pisa
Largo B. Pontecorvo, 3
56127 Pisa, Italy
Tel: +39 050 2212 710
paolo.mancarella@unipi.it
Francesca Toni
Department of Computing, Imperial College London
South Kensington Campus, Huxley Building
London SW7 2AZ, UK
Tel: +44 (0)20 7594 8228
ft@doc.ic.ac.uk
75