harm: a hybrid rule-based agent reputation model based on temporal defeasible logic

HARM A Hybrid Rule-based Agent Reputation Model based on Temporal Defeasible Logic

Kalliopi Kravari, Nick BassiliadesDepartment of Informatics

Aristotle University of ThessalonikiThessaloniki, Greece

RuleML 2012, Montpellier, Aug 27-29 2

OVERVIEW

Agents in the SW interact under uncertain and risky situations.

Whenever they have to interact with partners of whom they know nothing..

they have to make decision involving risk.

Thus:

Their success may depend on their ability to choose reliable partners.

Solution:

Reliable trust and/or reputation models.

SW evolutio

n

Intelligent Agents

SW Trust Layer

Nick Bassiliades


OVERVIEW

Trust is the degree of trust that can be invested in a certain agent.

Reputation is the opinion of the public towards an agent.

Reputation (trust) models provide the means to quantify reputation and trust

help agents to decide who to trust encourage trustworthy behavior deter dishonest participation

Current computational reputation models are usually built either on

interaction trust or witness reputation.

an agent’s direct experience

reports provided by others Nick Bassiliades


APPROACHES’ LIMITATIONS

If the reputation estimation is based only on direct experience, it would require a long time for an agent to reach a satisfying estimation level.

Why?

because when an agent enters an environment for the first time,

it has no history of interactions with the other agents in the environment.

If the reputation estimation is based only on witness reports, it could not guarantee reliable estimation.

Why?

because self-interested agents could be unwilling or unable

to sacrifice their resources in order to provide reports.

Nick Bassiliades


HYBRID MODELS

Hybrid models combine both interaction trust and witness reputation.

We propose HARM:

an incremental reputation model that combines the advantages of the hybrid reputation models the benefits of temporal defeasible logic (rule-based approach)

Nick Bassiliades


(Temporal) Defeasible Logic

Temporal defeasible logic (TDL) is an extension of defeasible logic (DL).

DL is a kind of non-monotonic reasoning

Why defeasible logic?

Rule-based, deterministic (without disjunction)

Enhanced representational capabilities

Classical negation used in rule heads and bodies

Negation-as-failure can be emulated

Rules may support conflicting conclusions

Skeptical: conflicting rules do not fire

Priorities on rules resolve conflicts among rules

Low computational complexityNick Bassiliades


Facts: e.g. student(Sofia)Strict Rules: e.g. student(X) person(X)Defeasible Rules: e.g. r: person(X) works(X)

r’: student(X) ¬works(X)Priority Relation between rules, e.g. r’ > r

Proof theory example: A literal q is defeasibly provable if:

supported by a rule whose premises are all defeasibly provable AND

q is not definitely provable ANDeach attacking rule is non-applicable or defeated by a

superior counter-attacking rule

Defeasible Logic

Nick Bassiliades


Two types of temporal literals: expiring temporal literals l:t (a literal l is valid for t time

instances) persistent temporal literals l@t

(a literal l is active after t time instances have passed and is valid thereafter)

temporal rules: a1:d1 ... an:dn d b:db

delay between the cause a1:d1 ... an:dn and the effect b:db

Example:(r1) => a@1 Literal a is

created due to r1.

(r2) a@1=>7 b:3 It becomes active at time offset 1.

It causes the head of r2 to be fired at time 8. The result b lasts only until time 10.

Thereafter, only the fact a remains.

Temporal Defeasible Logic

Nick Bassiliades


Evaluates four agent abilities: validity, completeness, correctness and response time.

An agent is valid if it is both sincere and credible. Sincere: believes what it says Credible: what it believes is true in the world

An agent is complete if it is both cooperative and vigilant. Cooperative: says what it believesVigilant: believes what is true in the world

An agent is correct if its provided service is correct with respect to a specification.

Response time is the time that an agent needs to complete the transaction.

HARM – Evaluated Abilities

Nick Bassiliades


Agent A establishes interaction with agent B:(A)Truster is the evaluating agent(B) Trustee is the evaluated agent

Truster’s rating value (r) in HARM has 8 coefficients:

2 IDs: Truster, Trustee 4 abilities: Validity, Completeness, Correctness,

Response time 2 weights: Confidence, Transaction value

Confidence: how confident the agent is for the rating Transaction value: how important the transaction was for

the agent

HARM - Ratings

Nick Bassiliades


Direct Experience (PRAX ) Indirect Experience

reports provided by strangers (SRAX) reports provided by known agents (e.g

friends) due to previous interactions (KRAX ) Both

Final reputation value of an agent X, required by an agent A:

RAX = {PRAX , KRAX, SRAX}

HARM – Experience Types

Nick Bassiliades


Sometimes one or more rating categories are missing.▫ e.g. a newcomer has no personal experience

A user is much more likely to believe statements from a trusted acquaintance than from a stranger. ▫ Thus, personal opinion (AX) is more valuable than strangers’

opinion (SX), as well as it is more valuable even from previously trusted partners (KX).

Superiority relationship among rating categories

HARM – Experience Types

KX

AX, KX, SX

AX, KX

AX, SX

KX, SX

AX

SX

Nick Bassiliades


RAX is a function that combines each available category▫ personal opinion (AX)▫ strangers’ opinion (SX)▫ previously trusted partners (KX)

HARM allows agents to define weights of ratings’ coefficients▫ Personal preferences

HARM – Final reputation value

, ,AX AX AXAXR PR KR SR

4 4 4

1 1 1

log log log, , ,

, , , _ 2

coefficient coefficient coefficienti AX i AX i AX

AX

i i ii i i

AVG w pr AVG w kr AVG w srR

w w w

coefficient validity completeness correctness response time

Nick Bassiliades


Truster’s rating (r) (defeasible RuleML / d-POSL syntax):

rating(id→rating’s_id, truster→truster’s_name, trustee→trustee’s_name, validity→value1,

completeness→value2, correctness→value3, response_time→value4, confidence→value5,

transaction_value→value6).

e.g. rating(id→1, truster→A, trustee→B, validity→5,

completeness→6, correctness→6, response_time→8, confidence→0.8,

transaction_value→0.9).

HARMRule-based Decision Making Mechanism / Facts

Nick Bassiliades


Confidence and transaction value allow us to decide how much attention we should pay on each rating.

It is important to take into account ratings that were made by confident trusters, since their ratings are more likely to be right.

Confident trusters, that were interacting in an important for them transaction, are even more likely to report truthful ratings.

HARMRule-based Decision Making Mechanism

Nick Bassiliades


r1: count_rating(rating→?idx, truster→?a, trustee→ ?x) :=

confidence_threshold(?conf), transaction_value_threshold(?tran), rating(id→?idx, confidence→?confx,

transaction_value→?tranx),

?confx >= ?conf, ?tranx >= ?tran.

r2: count_rating(…) :=

…?confx >= ?conf.

r3: count_rating(…) :=

…?tranx >= ?tran.

r1 > r2 > r3

HARMWhich ratings “count”?

• if both truster’s confidence and transaction importance are high, then that rating will be counted during the estimation process

• if the transaction value is lower than the threshold, it doesn’t matter so much if the truster’s confidence is high

• if there are only ratings with high transaction value, then they should be taken into account

• In any other case, the rating should be omitted.

Nick Bassiliades


All the previous rules are conclude positive literals. These literals are conflicting each other, for the

same pair of agents (truster and trustee)▫ We want in the presence e.g. of personal experience to omit

strangers’ ratings. ▫ That’s why there is also a superiority relationship between

the rules.

The conflict set is formally determined as follows:C[count_rating(truster→?a, trustee→?x)] =

{ ¬ count_rating(truster→?a, trustee→?x) } { count_rating(truster→?a1, trustee→?x1) | ?a ?a1 ∧ ?x

?x1 }

HARMConflicting Literals

Nick Bassiliades


r4: known(agent1→?a, agent2→?y) :-

count_rating(rating → ?id, truster→?a, trustee→?y).

r5: count_prAX(agent→?a, truster→?a, trustee→?x, rating→?id) :-

count_rating(rating → ?id, truster→? a, trustee→ ?x).

r6: count_krAX(agent→?a, truster→?k, trustee→?x, rating →?id) :-

known(agent1→?a, agent2→?k),

count_rating(rating→?id, truster→?k, trustee→ ?x).

r7: count_srAX(agent→?a, truster→?s, trustee→?x, rating→?id) :-

count_rating(rating → ?id, truster →?s, trustee→ ?x), not(known(agent1→?a, agent2→?s)).

Which agents are considered as known?

Categorization

of ratings

HARMDetermining Experience Types

Nick Bassiliades


Final step is to decide whose experience will “count”: direct, indirect (witness), or both.

The decision for RAX is based on a relationship theory

e.g. Theory #1: All categories count equally.

r8: participate(agent→?a, trustee→?x, rating→?id_ratingAX) :=

count_pr(agent→?a, trustee→?x, rating→ ?id_ratingAX).

r9: participate(agent→?a, trustee→?x, rating→?id_ratingKX) :=

count_kr(agent→?a, trustee→?x, rating→ ?id_ratingKX).

r10: participate(agent→?a, trustee→?x, rating→?id_ratingSX) :=

count_sr(agent→?a, trustee→?x, rating→ ?id_ratingSX).


Nick Bassiliades


HARMTheory #1: All categories count equally

KX

AX, KX, SX

AX, KX

AX, SX

KX, SX

AX

SX

Nick Bassiliades


e.g. Theory #2: An agent relies on its own experience if it believes it is sufficient. If not it acquires the opinions of others.







r8>r9>r10


Nick Bassiliades


HARMTheory #2: Personal experience is preferred to friends’ opinion to strangers’ opinion

KX

AX, KX, SX

AX, KX

AX, SX

KX, SX

AX

SX

Nick Bassiliades

>>


e.g. Theory #3:If direct experience is available (PRAX), then it is preferred to be combined with ratings from known agents (KRAX). If not, HARM acts as a pure witness system.







r8> r10, r9>r10


Nick Bassiliades


HARMTheory #3: Personal experience and friends’ opinion is preferred to strangers’ opinion

KX

AX, KX, SX

AX, KX

AX, SX

KX, SX

AX

SX

Nick Bassiliades

>


Agents may change their objectives at any time Evolution of trust over time should be taken into account Only the latest ratings participate in the reputation

estimation

In the temporal extension of HARM:each rating is a persistent temporal literal of TDLeach rule conclusion is an expiring temporal literal of TDL

The truster’s rating (r) is active after time_offset time instances have passed and is valid thereafter

rating(id→value1, truster→value2, trustee→ value3, validity→value4,

completeness→value5, correctness→value6,

response_time →value7, confidence→value8, transaction_value→value9)@time_offset.

HARM Temporal Defeasible Logic Extension

Nick Bassiliades


Rules are modified accordingly: each rating is active after t time instances have passed

(“@t”) each conclusion has a duration (“:duration”) each rule has a delay, which models the delay between the

cause and the effect. e.g. r1: count_rating(rating→?idx, truster→?a, trustee→ ?

x):duration := delay confidence_threshold(?conf),

transaction_value_threshold(?tran),

rating(id→?idx, confidence→?confx,

transaction_value→?tranx) @t,

?confx >= ?conf, ?tranx >= ?tran.

HARM Temporal Defeasible Logic Extension

Nick Bassiliades


HARM Evaluation

We implemented the model in EMERALD Framework for interoperating knowledge-

based intelligent agents in the SW. Built on JADE multi-agent platform

EMERALD uses Reasoners (agents offering reasoning services) Supports the DR-Device defeasible logic

system Used temporal predicates to simulate the

temporal semantics. o No available temporal defeasible logic reasoner

Nick Bassiliades


All agents provide the same service Performance is service – independent

Consumer agent selects the provider with the highest reputation value

HARM - Evaluation

Nick Bassiliades

Provider agentx

Consumer agent HARMAgent

Provider agenty

1. Request reputations of the provider agents

2. Inform about the provider with the highest reputation

3. Service request

5. Report rating

4. Service providing


Performance of providers (e.g. quality of service) is the utility that a consumer gains from each interaction Utility Gain (UG), UG [-10,

10]

Four models are used: HARM (rule-based / temporal) T-REX (temporal degradation) SERM (uses all history) NONE (no trust mechanism).

From previous study: CR, SPORAS (literature

famous, distributed)

HARM - Evaluation

HARM 5.73

T-REX 5.57

SERM 2.41

NONE 0.16

CR 5.48

SPORAS 4.65

Average UG per interaction

Nick Bassiliades

Number of simulation: 500 Number of providers: 100

Good providers 10

Ordinary providers 40

Intermittent providers 5

Bad providers 45


Conclusions

We proposed HARM that combines: the hybrid approach (interaction trust and witness

reputation)

the benefits of temporal defeasible logic (rule-based approach)

Overcomes the difficulty to locate witness reports (centralized administration authority)

It is the first reputation model that uses explicitly knowledge, in the form of defeasible logic, to predict agent’s future behavior Easy to simulate human decision making

Nick Bassiliades


Future Work Fully implement HARM with temporal defeasible

logic Compare HARM’s performance with other

centralized and decentralized models from the literature

Combine HARM and T-REX Develop a distributed version of HARM Verify its performance in real-world e-commerce

applications Combining it with Semantic Web metadata for

trust

Nick Bassiliades


Thank you!Any Questions?

Nick Bassiliades

harm: a hybrid rule-based agent reputation model based on temporal defeasible logic

Technology

previously

temporal defeasible

strangers

defeasible

strangers

sx ax

reputation

witness reputation