The design of intelligent agents has become a key issue for many interesting applications. Given that there is no universally accepted definition of intelligence, Russell developed the notion of rational agency as an alternative for the characterization of intelligent agency [11]. In short, an agent is said to be rational if it performs the right actions according to the information it possesses and the goals it wants to achieve.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Capobianco, Marcela

SEDICI - Repositorio de la UNLP

On the Effect of Dynamic Environments
in Defeasible Reasoning
Marcela Capobianco
Laboratorio de Investigación y Desarrollo en Inteligencia Artificial (LIDIA)
Departamento de Ciencias de la Computación
Universidad Nacional del Sur
e-mail: mc@cs.uns.edu.ar
1  Introduction
The design of intelligent agents has become a key issue for many interesting applications. Given that
there is no universally accepted definition of intelligence, Russell developed the notion of rational
agency as an alternative for the characterization of intelligent agency [11]. In short, an agent is said to
be rational if it performs the right actions according to the information it possesses and the goals it
wants to achieve.
Modeling the epistemic state of a rational agent is the most difficult enterprise that must be addressed
in its design process. Pereira et al. have endorsed the use of Extended Logic Programming (ELP) for
accomplishing this task [7]. In this setting, the knowledge of the agent is codified by a logic program
extended with explicit negation, and its beliefs are set by the well-founded semantics of this program.
This approach has a clear advantage: it admits a seamless transition between theory and practice.
Nevertheless, ELP cannot deal with incomplete and potentially contradictory information, an essential
capability in practical agents. Several argumentation formalisms [12,10,8,4,13] have been proposed as
knowledge representation and reasoning tools able to handle uncertain information. This property
elicits argument-based theories as proper tools for modeling the epistemic state of rational agents.
Unfortunately, argumentative systems lack the implementability of ELP.
Defeasible Logic Programming (DeLP) [5] is a new paradigm that interweaves the benefits of logic
programming and argument-based systems, thus providing a good trade-off between expressivity and
implementability. As a result, DeLP presents attractive properties to model the knowledge of rational
agents [1]. DeLP combines a language similar to that of logic programming with an inference process
based on argumentation. To answer a query q according to a defeasible logic program P the system
builds arguments for and against q. These arguments contend together in a dialectical dispute, in which
the outcome is established using a defeat criterion. The query q can be inferred from P if there exists
an argument for q that prevailed in the dispute.
Most applications for rational agents involve interacting with a dynamic world. To properly achieve
this interaction, the agent must be continuously adapting to the changes in its environment. In this
context, perception is a mandatory issue. We have tailored the DeLP system incorporating perceptions
abilities into a new formalism called Observation based DeLP (ODeLP). The language of ODeLP is
composed by a set of observations Ψ, encoding the knowledge the agent has about the world, and a set
of defeasible rules ∆, representing ways of extending the observations with tentative information (i.e.,
information that can be used if nothing is posed against it). The ODeLP program P structuring the
knowledge of the agent is able to express the following doxastic attitudes with respect a query q:
• Believe that q is true. In this case q belongs to the set of agent's beliefs.
• Believe that q is false, i.e., believe in the contrary of q.
• Neither believe that q is true nor that it is false.
Both the language and the inference process of ODeLP have been formally defined. We have also
developed an optimization of the ODeLP system that uses precompiled knowledge to speed up the
agent's reasoning. Since the cognitive process of rational agents is complex and computationally
expensive, the proposed optimization helps the agent to achieve a timely interaction with its
environment. This is essential for perceiving and acting on the world. In what follows we briefly
describe how to incorporate perception mechanisms and precompiled knowledge into the
ODeLP system.
2  Perception
John Pollock pioneered in the problem of perception in artificial agents. In Taking Perception
Seriously [9], Pollock claims that an agent residing in a complex dynamic environment cannot be
provided from its creation with all the information it needs. Therefore, the agent must be able to
perceive.
In our system, the task of perceiving can be carried out by any mechanism that detects the changes in
the world and reports the literals representing those changes. The specification of this mechanism
depends on the particular application domain and it is not addressed in our work. We only assume that
it cannot produce false observations. The perceived literals are added to the knowledge of the agent,
into the set of observations Ψ.
If new facts are carelessly added, Ψ may become inconsistent. To avoid this we have defined an
updating process [6] that removes any element of Ψ contradicting the new observation. Note that
according to our criterion, new perceptions are always preferred over older ones. There is a simple
reason behind this policy: given our initial assumption, both of the observations in disagreement were
correct at the time of their assimilation. As a result, the only explanation for the conflict is a change in
the state of world, and the new fact should be favored since it reflects the actual state. It is worth
mentioning that by updating the set of observations the agent can modify its beliefs, changing its
previous picture of the world when faced with new information.
3  Precompiled Knowledge
John Doyle defined the concept of Truth Maintenance Systems (TMS) [3] as tools for problems
solvers, inaugurating the use of precompiled knowledge. The function of a TMS is to record and
maintain the reasons for the agent's beliefs. Doyle describes a series of procedures that determine the
current set of beliefs from the recorded information and update it in accord with new reasons. He
remarked that the use of precompiled knowledge significantly improves the performance of the
system.
... the overhead required to record justifications for every program belief might seem
excessive. However, the pressing issue is not the expense of keeping records of the
sources of beliefs. Rather, we must consider the expense of not keeping these records.
If we throw away information about derivations, we may be condemning ourselves to
continually re-deriving information in large searches caused by changing irrelevant
assumptions ...
Doyle's proposal was a source of inspiration for our work: precompiled knowledge can also be used in
ODeLP to prevent the system from building the same argument from scratch several times. The key
idea can be resumed as follows. For a ODeLP program P = 〈Ψ, ∆〉 we maintain a repository
containing, among other things, every argument that could be built from rules in ∆. Using this
repository, we have defined a new optimized version of the inference process. For space reasons we
refer the interested reader to [2] for a complete description about the use of precompiled knowledge in
ODeLP.
4  Concluding Remarks
We have defined an expressive formalism that can be used to describe complex domains.
ODeLP provides mechanisms to update the knowledge of the agent from information acquired
perceptually, adapting it to the changes in the world. We have also shown that the use of precompiled
knowledge can improve the performance of ODeLP in the same way Truth Maintenance Systems
optimize problem solvers. Currently, we are researching on two different topics: studying the formal
properties of the ODeLP system and developing an efficient implementation of our formalism.
References
[1] Capobianco, M., and Chesñevar, C. I. Using Logic Programs to Model an Agent's
Epistemic State. In Proceedings of the 7th Workshop on Aspectos Teóricos de la Inteligencia
Artificial (ATIA), 2nd Workshop of Investigadores en Ciencias de la Computación (WICC)
(La Plata, May 2000), Universidad Nacional de La Plata.
[2] Capobianco, M., and Simari, G. R. Defeasible Reasoning in Dynamic Domains. In
Proceedings of the 1st Workshop en Agentes y Sistemas Inteligentes (WASI), 6th Congreso
Argentino de Ciencias de la Computación (CACIC) (Ushuaia, Oct. 2000), Universidad
Nacional de la Patagonia.
[3] Doyle, J. A Truth Maintenance System. Artificial Intelligence 12, 3 (1979), 231-272.
[4] Dung, P. M. On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic
Reasoning and Logic Programming and n-Person Games. Artificial Intelligence 77, 2 (1995),
321-357.
[5] García, A. J. La Programación en Lógica Rebatible: Lenguaje, Semántica Operacional, y
Paralelismo. PhD thesis, Universidad Nacional del Sur, Bahía Blanca, Argentina, Dec. 2000.
[6] Katsuno, H., and Mendelzon, A. On the difference between updating a knowledge base and
revising it. In Belief Revision, P.Gardenfors, Ed. Cambridge University Press, 1992, pp. 183-
203.
[7] Pereira, L. M., and Quaresma, P. Modelling Agent Interaction in Logic Programming. In
Proceedings of the 11th International Conference on Applications of Prolog (Sept. 1998),
O. Barenstein, Ed.
[8] Pollock, J. L. Cognitive Carpentry: a blueprint for how to build a person. Bradford/MIT
Press, 1995.
[9] Pollock, J. L. Taking Perception Seriously. In Proceedings of the 1st International
Conference on Autonomous Agents (Feb. 1997), pp. 526-527.
[10] Prakken, H., and Sartor, G. Argument-based extended logic programming with defeasible
priorities. Journal of Applied Non-classical Logics 7 (1997), 25-752.
[11] Russell, S. J. Rationality and Intelligence. Artificial Intelligence 94, 1-2 (1997), 57-77.
[12] Simari, G. R., and Loui, R. P. A Mathematical Treatment of Defeasible Reasoning and its
Implementation. Artificial Intelligence 53, 1-2 (1992), 125-157.
[13] Vreeswijk, G. Abstract Argumentation Systems. Artificial Intelligence 90, 1-2 (1997), 225-
279.


English

On the effect of dynamic environments in defeasible reasoning

Servicio de Difusión de la Creación Intelectual

On the Effect of Dynamic Environmentsin Defeasible ReasoningMarcela CapobiancoLaboratorio de Investigación y Desarrollo en Inteligencia Artificial (LIDIA)Departamento de Ciencias de la ComputaciónUniversidad Nacional del Sure-mail: mc@cs.uns.edu.ar1  IntroductionThe design of intelligent agents has become a key issue for many interesting applications. Given thatthere is no universally accepted definition of intelligence, Russell developed the notion of rationalagency as an alternative for the characterization of intelligent agency [11]. In short, an agent is said tobe rational if it performs the right actions according to the information it possesses and the goals itwants to achieve.Modeling the epistemic state of a rational agent is the most difficult enterprise that must be addressedin its design process. Pereira et al. have endorsed the use of Extended Logic Programming (ELP) foraccomplishing this task [7]. In this setting, the knowledge of the agent is codified by a logic programextended with explicit negation, and its beliefs are set by the well-founded semantics of this program.This approach has a clear advantage: it admits a seamless transition between theory and practice.Nevertheless, ELP cannot deal with incomplete and potentially contradictory information, an essentialcapability in practical agents. Several argumentation formalisms [12,10,8,4,13] have been proposed asknowledge representation and reasoning tools able to handle uncertain information. This propertyelicits argument-based theories as proper tools for modeling the epistemic state of rational agents.Unfortunately, argumentative systems lack the implementability of ELP.Defeasible Logic Programming (DeLP) [5] is a new paradigm that interweaves the benefits of logicprogramming and argument-based systems, thus providing a good trade-off between expressivity andimplementability. As a result, DeLP presents attractive properties to model the knowledge of rationalagents [1]. DeLP combines a language similar to that of logic programming with an inference processbased on argumentation. To answer a query q according to a defeasible logic program P the systembuilds arguments for and against q. These arguments contend together in a dialectical dispute, in whichthe outcome is established using a defeat criterion. The query q can be inferred from P if there existsan argument for q that prevailed in the dispute.Most applications for rational agents involve interacting with a dynamic world. To properly achievethis interaction, the agent must be continuously adapting to the changes in its environment. In thiscontext, perception is a mandatory issue. We have tailored the DeLP system incorporating perceptionsabilities into a new formalism called Observation based DeLP (ODeLP). The language of ODeLP iscomposed by a set of observations Ψ, encoding the knowledge the agent has about the world, and a setof defeasible rules ∆, representing ways of extending the observations with tentative information (i.e.,information that can be used if nothing is posed against it). The ODeLP program P structuring theknowledge of the agent is able to express the following doxastic attitudes with respect a query q:• Believe that q is true. In this case q belongs to the set of agent's beliefs.• Believe that q is false, i.e., believe in the contrary of q.• Neither believe that q is true nor that it is false.Both the language and the inference process of ODeLP have been formally defined. We have alsodeveloped an optimization of the ODeLP system that uses precompiled knowledge to speed up theagent's reasoning. Since the cognitive process of rational agents is complex and computationallyexpensive, the proposed optimization helps the agent to achieve a timely interaction with itsenvironment. This is essential for perceiving and acting on the world. In what follows we brieflydescribe how to incorporate perception mechanisms and precompiled knowledge into theODeLP system.2  PerceptionJohn Pollock pioneered in the problem of perception in artificial agents. In Taking PerceptionSeriously [9], Pollock claims that an agent residing in a complex dynamic environment cannot beprovided from its creation with all the information it needs. Therefore, the agent must be able toperceive.In our system, the task of perceiving can be carried out by any mechanism that detects the changes inthe world and reports the literals representing those changes. The specification of this mechanismdepends on the particular application domain and it is not addressed in our work. We only assume thatit cannot produce false observations. The perceived literals are added to the knowledge of the agent,into the set of observations Ψ.If new facts are carelessly added, Ψ may become inconsistent. To avoid this we have defined anupdating process [6] that removes any element of Ψ contradicting the new observation. Note thataccording to our criterion, new perceptions are always preferred over older ones. There is a simplereason behind this policy: given our initial assumption, both of the observations in disagreement werecorrect at the time of their assimilation. As a result, the only explanation for the conflict is a change inthe state of world, and the new fact should be favored since it reflects the actual state. It is worthmentioning that by updating the set of observations the agent can modify its beliefs, changing itsprevious picture of the world when faced with new information.3  Precompiled KnowledgeJohn Doyle defined the concept of Truth Maintenance Systems (TMS) [3] as tools for problemssolvers, inaugurating the use of precompiled knowledge. The function of a TMS is to record andmaintain the reasons for the agent's beliefs. Doyle describes a series of procedures that determine thecurrent set of beliefs from the recorded information and update it in accord with new reasons. Heremarked that the use of precompiled knowledge significantly improves the performance of thesystem.... the overhead required to record justifications for every program belief might seemexcessive. However, the pressing issue is not the expense of keeping records of thesources of beliefs. Rather, we must consider the expense of not keeping these records.If we throw away information about derivations, we may be condemning ourselves tocontinually re-deriving information in large searches caused by changing irrelevantassumptions ...Doyle's proposal was a source of inspiration for our work: precompiled knowledge can also be used inODeLP to prevent the system from building the same argument from scratch several times. The keyidea can be resumed as follows. For a ODeLP program P = 〈Ψ, ∆〉 we maintain a repositorycontaining, among other things, every argument that could be built from rules in ∆. Using thisrepository, we have defined a new optimized version of the inference process. For space reasons werefer the interested reader to [2] for a complete description about the use of precompiled knowledge inODeLP.4  Concluding RemarksWe have defined an expressive formalism that can be used to describe complex domains.ODeLP provides mechanisms to update the knowledge of the agent from information acquiredperceptually, adapting it to the changes in the world. We have also shown that the use of precompiledknowledge can improve the performance of ODeLP in the same way Truth Maintenance Systemsoptimize problem solvers. Currently, we are researching on two different topics: studying the formalproperties of the ODeLP system and developing an efficient implementation of our formalism.References[1] Capobianco, M., and Chesñevar, C. I. Using Logic Programs to Model an Agent'sEpistemic State. In Proceedings of the 7th Workshop on Aspectos Teóricos de la InteligenciaArtificial (ATIA), 2nd Workshop of Investigadores en Ciencias de la Computación (WICC)(La Plata, May 2000), Universidad Nacional de La Plata.[2] Capobianco, M., and Simari, G. R. Defeasible Reasoning in Dynamic Domains. InProceedings of the 1st Workshop en Agentes y Sistemas Inteligentes (WASI), 6th CongresoArgentino de Ciencias de la Computación (CACIC) (Ushuaia, Oct. 2000), UniversidadNacional de la Patagonia.[3] Doyle, J. A Truth Maintenance System. Artificial Intelligence 12, 3 (1979), 231-272.[4] Dung, P. M. On the Acceptability of Arguments and its Fundamental Role in NonmonotonicReasoning and Logic Programming and n-Person Games. Artificial Intelligence 77, 2 (1995),321-357.[5] García, A. J. La Programación en Lógica Rebatible: Lenguaje, Semántica Operacional, yParalelismo. PhD thesis, Universidad Nacional del Sur, Bahía Blanca, Argentina, Dec. 2000.[6] Katsuno, H., and Mendelzon, A. On the difference between updating a knowledge base andrevising it. In Belief Revision, P.Gardenfors, Ed. Cambridge University Press, 1992, pp. 183-203.[7] Pereira, L. M., and Quaresma, P. Modelling Agent Interaction in Logic Programming. InProceedings of the 11th International Conference on Applications of Prolog (Sept. 1998),O. Barenstein, Ed.[8] Pollock, J. L. Cognitive Carpentry: a blueprint for how to build a person. Bradford/MITPress, 1995.[9] Pollock, J. L. Taking Perception Seriously. In Proceedings of the 1st InternationalConference on Autonomous Agents (Feb. 1997), pp. 526-527.[10] Prakken, H., and Sartor, G. Argument-based extended logic programming with defeasiblepriorities. Journal of Applied Non-classical Logics 7 (1997), 25-752.[11] Russell, S. J. Rationality and Intelligence. Artificial Intelligence 94, 1-2 (1997), 57-77.[12] Simari, G. R., and Loui, R. P. A Mathematical Treatment of Defeasible Reasoning and itsImplementation. Artificial Intelligence 53, 1-2 (1992), 125-157.[13] Vreeswijk, G. Abstract Argumentation Systems. Artificial Intelligence 90, 1-2 (1997), 225-279.

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On the effect of dynamic environments in defeasible reasoning

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