Updating Agents

1
Updating, preferring and actingin abductive
agents

• P. DellAcqua
• Dept. of Science and Technology - ITN
• Linköping University, Sweden

Joint work with

• L. M. Pereira, J. J. Alferes, J. A. Leite
• Centro de Inteligência Artificial - CENTRIA
• Universidade Nova de Lisboa, Portugal

June 12th, 2003
LAquila, Italy
2
Outline

• Updating agents
• Agents language
• Preferring in agents
• Updates plus preferring
• Architecture Architecture
• Future works

3
Capabilities of our agents

• We propose an approach to agents that can
• - reason and react to the environment (including
  other agents)
• - update their own knowledge, reactions and
  goals
• - interact by updating the theory of another
  agent
• - decide whether to accept an update depending
  on the requesting agent
• - prefer among possible choices
• - abduce hypotheses to explain observations

4
Updating agents

• Updating agent a rational, reactive agent that
  can dynamically change its own knowledge and goals

- makes observations - updates its knowledge,
reactions and goals - thinks a bit (rational) -
selects and executes an action (reactive)
5
Agents language

• Atomic formulae

A objective atoms not A default atoms
iC projects i?C updates
Formulae
generalized rules
Li is an atom, an update or a negated update
A L1 Ù ... Ù Ln not A L1 Ù ... Ù Ln
Zj is a project
integrity constraint
false L1 Ù ... Ù Ln Ù Z1 Ù ... Ù Zm
active rule
L1 Ù ... Ù Ln ? Z
6
Projects and updates
A project jC denotes the intention of some
agent i of proposing the updating the theory of
agent j with C. i?C denotes an update proposed
by i of the current theory of some agent j with C.
fred?C
wilmaC
7
Example active rules
Consider the following active rules in the theory
of Maria.
money ? maria not work beach ? maria
goToBeach travelling ? pedro bookTravel
8
Projects
A project iC can take one of the forms
i ( A L1 Ù ... Ù Ln )
i ( not A L1 Ù ... Ù Ln )
i ( false L1 Ù ... Ù Ln Ù Z1 Ù ... Ù Zm )
i ( L1 Ù ... Ù Ln ? Z )
i ( ?- L1 Ù ... Ù Ln )
Note that a program can be updated with another
program i.e., any rule can be updated.
9
Agents knowledge states

• Knowledge states represent dynamically evolving
  states of agents knowledge. They undergo change
  due to updates.

Given the current knowledge state Ps , its
successor knowledge state Ps1 is produced as a
result of the occurrence of a set of parallel
updates.
Update actions do not modify the current or any
of the previous knowledge states. They only
affect the successor state the precondition of
the action is evaluated in the current state and
the postcondition updates the successor state.
10
Enabling agents to prefer
Let the underlying theory of Maria be
city not mountain Ù not beach Ù not
travelling work vacation not work
mountain not city Ù not beach Ù not travelling
Ù money beach not city Ù not mountain Ù not
travelling Ù money travelling not city Ù not
mountain Ù not beach Ù money
Since the theory has a unique two-valued
model Mcity, work Maria decides to live in
the city.
11
Enabling agents to prefer
If we add the fact money to the theory of Maria,
then the theory has 4 models M1city, money,
work M2 mountain, money, work M3 beach,
money, work M4 travelling, money, work
Therefore, Maria is unable to decide where to
live.
To select among alternative choices, Maria needs
the ability of preferring.
12
Updates plus preferences

• A logic programming framework that combines two
  distinct forms of reasoning preferring and
  updating.

A language capable of considering sequences of
logic programs that result from the consecutive
updates of an initial program, where it is
possible to define a priority relation among the
rules of all successive programs.
Updates create new models, while preferences
allow us to select among pre-existing models
The priority relation can itself be updated.
13
Preferring agents
Preferring agent an agent that is able to
prefer beliefs and reactions when several
alternatives are possible.

• Agents can express preferences about their own
  rules.
• Preferences are expressed via priority rules.
• Preferences can be updated, possibly on advice
  from others.

14
Priority rules
Let lt be a binary predicate symbol whose set of
constants includes all the generalized rules r1
lt r2 means that the rule r1 is preferred to rule
r2 .
A priority rule is a generalized rule defining lt
.

• A prioritized LP is a set of generalized rules
  (possibly, priority rules) and integrity
  constraints.

15
Example a prioritized LP
(1) city not mountain Ù not beach Ù not
travelling (2) work (3) vacation not work (4)
mountain not city Ù not beach Ù not travelling
Ù money (5) beach not city Ù not mountain Ù
not travelling Ù money (6) travelling not city
Ù not mountain Ù not beach Ù money
1lt4 work 4lt6 vacation 1lt5 work 5lt6
vacation 1lt6 work 6lt1 vacation
If we add money to the theory, then there is a
unique model
Mcity, money, work
If work is false, then vacation holds
M1mountain, money, vacation
M2beach, money, vacation
16
Agent theory

• The initial theory of an agent ? is a pair
  (P,R)
• - P is an prioritized LP.
• - R is a set of active rules.

An updating program is a finite set of updates.
Let S be a set of natural numbers. We call the
elements s?S states.
An agent ? at state s , written ??s , is a pair
(T,U) - T is the initial theory of ?. - UU1,,
Us is a sequence of updating programs.
17
Example happy story
Let the initial theory (P,R) of Maria be
State 0
(1) city not mountain Ù not beach Ù not
travelling (2) work (3) vacation not work (4)
mountain not city Ù not beach Ù not travelling
Ù money (5) beach not city Ù not mountain Ù
not travelling Ù money (6) travelling not city
Ù not mountain Ù not beach Ù mone
1lt4 work 4lt6 vacation 1lt5 work 5lt6
vacation 1lt6 work 6lt1 vacation
money ? maria not work beach ? maria
goToBeach travelling ? pedro bookTravel
U
18
Example happy story
At state 0 Maria receives l?money
State 1
(1) city not mountain Ù not beach Ù not
travelling (2) work (3) vacation not work (4)
mountain not city Ù not beach Ù not travelling
Ù money (5) beach not city Ù not mountain Ù
not travelling Ù money (6) travelling not city
Ù not mountain Ù not beach Ù mone
1lt4 work 4lt6 vacation 1lt5 work 5lt6
vacation 1lt6 work 6lt1 vacation
money ? maria not work beach ? maria
goToBeach travelling ? pedro bookTravel
U U1l?money
19
Example happy story
Then, Maria receives maria?not work
State 2
(1) city not mountain Ù not beach Ù not
travelling (2) work (3) vacation not work (4)
mountain not city Ù not beach Ù not travelling
Ù money (5) beach not city Ù not mountain Ù
not travelling Ù money (6) travelling not city
Ù not mountain Ù not beach Ù mone
1lt4 work 4lt6 vacation 1lt5 work 5lt6
vacation 1lt6 work 6lt1 vacation
money ? maria not work beach ? maria
goToBeach travelling ? pedro bookTravel
UU1l?money, U2maria?not work
20
Example happy story
Then, Maria receives f ? (5lt4vacation)
State 3
(1) city not mountain Ù not beach Ù not
travelling (2) work (3) vacation not work (4)
mountain not city Ù not beach Ù not travelling
Ù money (5) beach not city Ù not mountain Ù
not travelling Ù money (6) travelling not city
Ù not mountain Ù not beach Ù mone
1lt4 work 4lt6 vacation 1lt5 work 5lt6
vacation 1lt6 work 6lt1 vacation
money ? maria not work beach ? maria
goToBeach travelling ? pedro bookTravel
UU1l?money, U2maria?not work, U3f
?(5lt4vacation)
21
Multi-agent systems
A multi-agent system M??1s ,, ??ns at state
s is a set of agents ?1,,?n at state s.
M characterizes a fixed society of evolving
agents.
The declarative semantics of M characterizes the
relationship among the agents in M and how the
system evolves.
The declarative semantics is stable models based.
22
Agent architecture
Java
Control Cycle
InterProlog
InterProlog
Rational P
Reactive PR
can abduce
cannot abduce
XSB Prolog
XSB Prolog
23
Agent architectures implementation
by Mattias Engberg
24
Fw logic-based controllers

• Use of agents as logic-based controllers
• (joint work with A. Lombardi)
• - simple artificial world balloon environment

25
Fw agent organizational structures

• Formalize organizational structures for epistemic
  multi-agent systems (eMAS)
• - groups, institutions, societies, etc.
• - norms, regulations, etc.
• Ongoing implementation of a platform supporting
  the interactions of our logic-based agents as
  well as some forms of agent structures (by
  Mattias Blixt)

26
Fw user preference information in query answering
Use of preference reasoning at query time to
facilitate the retrieval of information wrt.
users interests (joint work with A. Vitória)

• how to incorporate abduction
• abductive preferences leading to conditional
  answers depending on accepting a preference

• group preference
• how to tackle the problem arising when we have
  several users query the system together

27
Applications

• Applications in which our agent technology can
  have a significant potential to contribute are
  web applications, e.g.

• information integration
• how to integrate data from multiple
  heterogeneous sources and to provide a uniform
  interface, e.g. to provide info about movies

• web-site management
• self-reconfigurable and adaptive web sites

declarative representation of web sites
allows - to automatically reconstruct them,
e.g. on usage patterns and can adapt
themselves wrt. user profiles - to enforce
integrity constraints on web sites, e.g. no
dangling pointers