1. Analysing Norms with Transition Systems
Trevor Bench-Capon
Department of Computer Science
University of Liverpool

2. Designing Norms Somewhat neglected in AI and Law
Much interest in Multi Agent Systems
Electronic Institutions
Open Systems
MAS tend to use transition diagrams and a logic like CTL
Are the techniques useful in AI and Law?

3. ICAIL 1995
Radboud Winkels and Nienke den Haan: Automated legislative drafting: generating paraphrases of legislation, ICAIL 1995.
Use of “deep models” to support legislative drafting
Four dimensions:
Default status: desirable, undesirable
Viewpoint: legislator, norm subjects
Abstraction: high, low
Deontic operators: permission, prohibition, obligation

4. Multi Agent Systems State 4
Use Transition Diagrams: often joint actions, one action per relevant agent. Coalition Temporal Logic is often used.
State 1
a,b
State 2
c,d
a,d
a,b
State 3
State 4
c,d

5. Trains Two trains, one east bound, one westbound.
Normally two track, but single track in tunnel.

6. Train viewpoint
Can move or wait
Entering the tunnel could crash

7. System Viewpoint
Two trains, joint actions.
CRASH

8. Machine Gunners Dilemma
Two machine Gunners are attacked by a superior force.
If both run, enemy wins and both are taken prisoner
If one runs and one stays, the deserter escapes and the other dies, but delays the enemy
If both stay either both die or both survive: either way enemy is delayed sufficiently.

9. Three Ways to Support a Norm
Ullmann-Margalit Emergence of Norms
Enforcement – make it impossible to disobey
Chain the soldiers to their guns
Sanctions – punish disobedience
Shoot deserters
Encourage obedience – regimental pride, comradeship, confidence in the regiment
Tradition, training, discipline

10. Enforcement: Prohibition in MAS
Remove a Transition.
For example: prohibit c in State 1:
State 1
a,b
State 2
c,d
a,d
a,b
State 3
State 4
c,d

11. Enforcement : Prohibition in MAS
Remove a Transition.
For example: prohibit c in State 1:
State 1
a,b
State 2
a,d
a,b
State 3
State 4
c,d

12. Enforcement : Obligation in MAS
To obligate an action remove all other links.
E.g. obligate c in State 1
State 1
a,b
State 2
c,d
a,d
a,b
State 3
State 4
c,d

13. Enforcement : Obligation in MAS
To obligate an action remove all other links.
E.g. obligate c in State 1
State 1
State 2
c,d
a,b
State 3
State 4
c,d

14. Solution to Train Problem
If a train is in the tunnel it must move
The westbound train can only enter the tunnel when the eastbound is away
Gives priority to the eastbound train: the westbound may have to wait a turn or two (relies on the desire of the eastbound train to move)
Waiting is worthwhile for the certainty
If there were many states in the circuit, the wait might be a long one.

15. Enforcement With enforcement Compliance is ensured
States are unchanged
Some transitions are removed
Options are reduced, but unaltered
Compliance is ensured
But sometimes violation is desired (breaking a red light in an emergency)
Enforcement is often impractical (open systems, human agents)
Ok for software systems?
Good for model checking to show that norms are effective (assuming total compliance)

16. States and Actions
In enforcement (and generally in MAS) the objectives is to avoid an undesirable state, but the norm forbids an action. Why?
Often we are unsure what will result from an action:
The action may have a variable effect (throwing a dice)
Outcome may depend on what others choose to do
But particular states can be prevented by prohibiting actions (removes risk)

17. Enforcement: Prohibition in MAS
Suppose State 2 is undesirable
Prohibit a in State 1:
State 1
a,b
State 2
c,d
a,d
a,b
State 3
State 4
c,d

18. Enforcement : Prohibition in MAS
Suppose State 2 is undesirable
Prohibit a in State 1:
State 1
a,b
State 2
State 3 is prevented
As a side
effect
c,d
a,d
a,b
State 3
State 4
c,d

19. Sanctions Sanctions need:
An additional agent to represent the sanction enforcer
Additional states to represent the effect of the sanctions
If always enforced sanctions make the undesirable states impossible
Nonexistent rather than unreachable

20. Train Example
Can still crash, but
now there is a fine

21. Types of Sanction Compensation Deterrent
The sanction makes the situation acceptable (perhaps even preferable). The norm subject may regard it as a fee
Library fines, parking fines.
Deterrent
The situation remains unacceptable: the sanction should make it unacceptable to the norm subject also

22. Imposing Sanctions
In some cases (such as library fines) the sanction can always be imposed
But in many cases it is possible that the sanction won’t be enforced – the violator will get away with it.
In such cases the sanction has to be more severe, so that the expected cost, taking into account the probability of enforcement, will be at the desired level (for compensation or deterrence)
Can operate with rewards for compliance rather than sanctions for violation

23. Norms Without Sanctions
Norms of coordination
Subjects have no preference, or also wish to avoid the bad situation. The norm provides the information about what others will do so that the bad situation can be avoided
In some cases (e.g. traffic priorities) the subject may be willing to pay a small cost for the certainty
Compliance itself is a value or Violation has a stigma
This makes following the norm more attractive (cf. Regimental pride), or violating the norm less attractive (peer pressure)

24. Some Differences
Behaviour of an individual versus interaction between several individuals.
The result of an action may be certain or uncertain
Deliberate violation versus risk of violation
Uncertainty may be because of the outcome is probabilistic, or because others influence outcome or because the current state is uncertain
Proscribed situation may or may not be desirable to the norm subject.
If undesirable, a coordination norm is usually sufficient

25. Representing Norms Removing transitions to ensure compliance
Adding an “enforcement agent” and changing nodes to represent sanctions
Sanctions can be
Fees
Compensation
Deterrent
By introducing additional values to guide choices
Make the behaviour of others more predictable
Make the situation more or less desirable

26. Summary
Design and analysis of norms has been under explored in AI and Law
It is an important topic in MAS, and a number of techniques have emerged, but these are often
Over simplified
Applicable to software agents, but not real agents
I have offered some observations and a framework which I hope will be built on