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Organization-Oriented Programming inMulti-Agent Systems
Andreas Schmidt Jensen
DTU Informatics
November 29, 2012
Algolog Multi-Agent Programming Seminar 2012
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 1 / 22
Outline
Outline
1 Agent- and Organization-Centered MAS
2 Conflicting decision influences
3 A Logic for Qualitative Decision Theory
4 Modelling influences and consequences
5 A prototype
6 Conclusion
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 2 / 22
Agent- and Organization-Centered MAS Agent-Centered Multi-Agent Systems
Agent-Centered Multi-Agent Systems
Agents are free to communicate with any other agent.
All of the agent’s services are available to every other agent.
The agent itself is responsible for constraining its accessibility fromother agents.
The agent should itself define its relation and contracts with otheragents.
Agents are supposed to be autonomous and no constraints are put onthe way they interact.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 3 / 22
Agent- and Organization-Centered MAS Agent-Centered Multi-Agent Systems
Agent-Centered Multi-Agent Systems
Agents are free to communicate with any other agent.
All of the agent’s services are available to every other agent.
The agent itself is responsible for constraining its accessibility fromother agents.
The agent should itself define its relation and contracts with otheragents.
Agents are supposed to be autonomous and no constraints are put onthe way they interact.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 3 / 22
Agent- and Organization-Centered MAS Agent-Centered Multi-Agent Systems
Agent-Centered Multi-Agent Systems
Agents are free to communicate with any other agent.
All of the agent’s services are available to every other agent.
The agent itself is responsible for constraining its accessibility fromother agents.
The agent should itself define its relation and contracts with otheragents.
Agents are supposed to be autonomous and no constraints are put onthe way they interact.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 3 / 22
Agent- and Organization-Centered MAS Agent-Centered Multi-Agent Systems
Agent-Centered Multi-Agent Systems
Agents are free to communicate with any other agent.
All of the agent’s services are available to every other agent.
The agent itself is responsible for constraining its accessibility fromother agents.
The agent should itself define its relation and contracts with otheragents.
Agents are supposed to be autonomous and no constraints are put onthe way they interact.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 3 / 22
Agent- and Organization-Centered MAS Agent-Centered Multi-Agent Systems
Agent-Centered Multi-Agent Systems
Agents are free to communicate with any other agent.
All of the agent’s services are available to every other agent.
The agent itself is responsible for constraining its accessibility fromother agents.
The agent should itself define its relation and contracts with otheragents.
Agents are supposed to be autonomous and no constraints are put onthe way they interact.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 3 / 22
Agent- and Organization-Centered MAS Organization-Centered Multi-Agent Systems
Organization-Centered Multi-Agent Systems
Principle 1: The organizational level describes the “what” and not the“how”.
Principle 2: The organization provides only descriptions of expectedbehavior. There are no agent description and therefore nomental issues at the organizational level.
Principle 3: An organization provides a way for partitioning a system,each partition constitutes a context of interaction for agents.
Organizationalstructure
Reasoning aboutorganization
Enteringthe organization
Enactingroles
Leavingthe organization
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 4 / 22
Agent- and Organization-Centered MAS Organization-Centered Multi-Agent Systems
Organization-Centered Multi-Agent Systems
Principle 1: The organizational level describes the “what” and not the“how”.
Principle 2: The organization provides only descriptions of expectedbehavior. There are no agent description and therefore nomental issues at the organizational level.
Principle 3: An organization provides a way for partitioning a system,each partition constitutes a context of interaction for agents.
Organizationalstructure
Reasoning aboutorganization
Enteringthe organization
Enactingroles
Leavingthe organization
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 4 / 22
Agent- and Organization-Centered MAS Organization-Centered Multi-Agent Systems
Organization-Centered Multi-Agent Systems
Principle 1: The organizational level describes the “what” and not the“how”.
Principle 2: The organization provides only descriptions of expectedbehavior. There are no agent description and therefore nomental issues at the organizational level.
Principle 3: An organization provides a way for partitioning a system,each partition constitutes a context of interaction for agents.
Organizationalstructure
Reasoning aboutorganization
Enteringthe organization
Enactingroles
Leavingthe organization
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 4 / 22
Agent- and Organization-Centered MAS Organization-Centered Multi-Agent Systems
Organization-Centered Multi-Agent Systems
Principle 1: The organizational level describes the “what” and not the“how”.
Principle 2: The organization provides only descriptions of expectedbehavior. There are no agent description and therefore nomental issues at the organizational level.
Principle 3: An organization provides a way for partitioning a system,each partition constitutes a context of interaction for agents.
Organizationalstructure
Reasoning aboutorganization
Enteringthe organization
Enactingroles
Leavingthe organization
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 4 / 22
Agent- and Organization-Centered MAS Organization-Centered Multi-Agent Systems
Organization-Centered Multi-Agent Systems
Principle 1: The organizational level describes the “what” and not the“how”.
Principle 2: The organization provides only descriptions of expectedbehavior. There are no agent description and therefore nomental issues at the organizational level.
Principle 3: An organization provides a way for partitioning a system,each partition constitutes a context of interaction for agents.
Organizationalstructure
Reasoning aboutorganization
Enteringthe organization
Enactingroles
Enactingroles
Leavingthe organization
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 4 / 22
Conflicting decision influences
Conflicting decision influences
Agent DesiresObligations
An agent, Alice, has a desire to stay at home, but an obligation towardsher employer to go to work. What should she do? She knows that she willget fired if she violates her obligation.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 5 / 22
Conflicting decision influences
Conflicting decision influences
Agent DesiresObligations
An agent, Alice, has a desire to stay at home, but an obligation towardsher employer to go to work. What should she do? She knows that she willget fired if she violates her obligation.
Suggestion: A priori ordering.
Desires before obligations → Selfish agent
Obligations before desires → Social agent
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 5 / 22
Conflicting decision influences
Conflicting decision influences
Agent DesiresObligations
An agent, Alice, has a desire to stay at home, but an obligation towardsher employer to go to work. What should she do? She knows that she willget fired if she violates her obligation.
Consider the consequences of bringing about a state.
work → ¬fired
¬work → fired
If the agent prefers not getting fired, then clearly it should work.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 5 / 22
A Logic for Qualitative Decision Theory
A Logic for Qualitative Decision Theory
Idea: Order possible worlds according to
each agent’s own preference
An agent prefers sunny weather.
which worlds are most normal (or expected)
Normally it is not sunny when it is raining.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 6 / 22
A Logic for Qualitative Decision Theory
A Logic for Qualitative Decision Theory
Idea: Order possible worlds according to
each agent’s own preference
An agent prefers sunny weather.
which worlds are most normal (or expected)
Normally it is not sunny when it is raining.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 6 / 22
A Logic for Qualitative Decision Theory
A Logic for Qualitative Decision Theory
Idea: Order possible worlds according to
each agent’s own preference
An agent prefers sunny weather.
which worlds are most normal (or expected)
Normally it is not sunny when it is raining.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 6 / 22
A Logic for Qualitative Decision Theory
A Logic for Qualitative Decision Theory
Idea: Order possible worlds according to
each agent’s own preference
An agent prefers sunny weather.
which worlds are most normal (or expected)
Normally it is not sunny when it is raining.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 6 / 22
A Logic for Qualitative Decision Theory
Extended QDT-model
M = 〈W ,Ag ,≤1P , . . . ,≤n
P ,≤N , π〉
Propositional operators: ¬, ∧Modal operators:
2iPϕ: ϕ is true in all agent i ’s more preferred worlds.←2i
Pϕ: ϕ is true in all agent i ’s less preferred worlds.
2Nϕ: ϕ is true in all more normal worlds.←2Nϕ: ϕ is true in all less normal worlds.
Truth in all worlds:↔2i
Pϕ = 2iPϕ ∧
←2i
Pϕ, similar for normality.
3-operators are defined as usual (i.e. 3iPϕ = ¬2i
P¬ϕ etc).
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 7 / 22
A Logic for Qualitative Decision Theory
Extended QDT-model
M = 〈W ,Ag ,≤1P , . . . ,≤n
P ,≤N , π〉
Propositional operators: ¬, ∧Modal operators:
2iPϕ: ϕ is true in all agent i ’s more preferred worlds.←2i
Pϕ: ϕ is true in all agent i ’s less preferred worlds.
2Nϕ: ϕ is true in all more normal worlds.←2Nϕ: ϕ is true in all less normal worlds.
Truth in all worlds:↔2i
Pϕ = 2iPϕ ∧
←2i
Pϕ, similar for normality.
3-operators are defined as usual (i.e. 3iPϕ = ¬2i
P¬ϕ etc).
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 7 / 22
A Logic for Qualitative Decision Theory
Extended QDT-model
M = 〈W ,Ag ,≤1P , . . . ,≤n
P ,≤N , π〉
Propositional operators: ¬, ∧Modal operators:
2iPϕ: ϕ is true in all agent i ’s more preferred worlds.←2i
Pϕ: ϕ is true in all agent i ’s less preferred worlds.
2Nϕ: ϕ is true in all more normal worlds.←2Nϕ: ϕ is true in all less normal worlds.
Truth in all worlds:↔2i
Pϕ = 2iPϕ ∧
←2i
Pϕ, similar for normality.
3-operators are defined as usual (i.e. 3iPϕ = ¬2i
P¬ϕ etc).
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 7 / 22
A Logic for Qualitative Decision Theory
Extended QDT-model
M = 〈W ,Ag ,≤1P , . . . ,≤n
P ,≤N , π〉
Propositional operators: ¬, ∧Modal operators:
2iPϕ: ϕ is true in all agent i ’s more preferred worlds.←2i
Pϕ: ϕ is true in all agent i ’s less preferred worlds.
2Nϕ: ϕ is true in all more normal worlds.←2Nϕ: ϕ is true in all less normal worlds.
Truth in all worlds:↔2i
Pϕ = 2iPϕ ∧
←2i
Pϕ, similar for normality.
3-operators are defined as usual (i.e. 3iPϕ = ¬2i
P¬ϕ etc).
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 7 / 22
A Logic for Qualitative Decision Theory
Extended QDT-model
M = 〈W ,Ag ,≤1P , . . . ,≤n
P ,≤N , π〉
Propositional operators: ¬, ∧Modal operators:
2iPϕ: ϕ is true in all agent i ’s more preferred worlds.←2i
Pϕ: ϕ is true in all agent i ’s less preferred worlds.
2Nϕ: ϕ is true in all more normal worlds.←2Nϕ: ϕ is true in all less normal worlds.
Truth in all worlds:↔2i
Pϕ = 2iPϕ ∧
←2i
Pϕ, similar for normality.
3-operators are defined as usual (i.e. 3iPϕ = ¬2i
P¬ϕ etc).
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 7 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Semantics
Semantics
M,w |= p ⇐⇒ p ∈ π(w)
M,w |= ¬ϕ ⇐⇒ M,w 6|= ϕ
M,w |= ϕ ∧ ψ ⇐⇒ M,w |= ϕ ∧M,w |= ψ
M,w |= 2iP ϕ ⇐⇒ ∀v ∈W , v ≤i
P w ,M, v |= ϕ
M,w |= ←2i
P ϕ ⇐⇒ ∀v ∈W ,w <iP v ,M, v |= ϕ
M,w |= 2N ϕ ⇐⇒ ∀v ∈W , v ≤N w ,M, v |= ϕ
M,w |= ←2N ϕ ⇐⇒ ∀v ∈W ,w <N v ,M, v |= ϕ
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 8 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
I (B | A) ≡ ↔2i
P¬A ∨↔3iP(A ∧2i
P(A→ B)) (Conditional preference)
A ≤iP B ≡ ↔
2iP(B → 3i
PA) (Relative preference)
T (B | A) ≡ ¬I (¬B | A) (Conditional tolerance)
A⇒ B ≡ ↔2N¬A ∨↔3N(A ∧2N(A→ B)) (Normative conditional)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 9 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
I (B | A) ≡ ↔2i
P¬A ∨↔3iP(A ∧2i
P(A→ B)) (Conditional preference)
A ≤iP B ≡ ↔
2iP(B → 3i
PA) (Relative preference)
T (B | A) ≡ ¬I (¬B | A) (Conditional tolerance)
A⇒ B ≡ ↔2N¬A ∨↔3N(A ∧2N(A→ B)) (Normative conditional)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 9 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
I (B | A) ≡ ↔2i
P¬A ∨↔3iP(A ∧2i
P(A→ B)) (Conditional preference)
A ≤iP B ≡ ↔
2iP(B → 3i
PA) (Relative preference)
T (B | A) ≡ ¬I (¬B | A) (Conditional tolerance)
A⇒ B ≡ ↔2N¬A ∨↔3N(A ∧2N(A→ B)) (Normative conditional)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 9 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
I (B | A) ≡ ↔2i
P¬A ∨↔3iP(A ∧2i
P(A→ B)) (Conditional preference)
A ≤iP B ≡ ↔
2iP(B → 3i
PA) (Relative preference)
T (B | A) ≡ ¬I (¬B | A) (Conditional tolerance)
A⇒ B ≡ ↔2N¬A ∨↔3N(A ∧2N(A→ B)) (Normative conditional)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 9 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
P 6≤iP Q ≡ ¬(P ≤i
P Q) (Not as preferred)
P <iP Q ≡ (P ≤i
P Q ∧ Q 6≤iP P) (Strictly preferred)
P ≈iP Q ≡ (P ≤i
P Q ∧ Q ≤iP P)
∨ (P 6≤iP Q ∧ Q 6≤i
P P) (Equally preferred)
A ≤iT (C) B ≡ (T (A | C ) ∧ ¬T (B | C )) ∨
((T (A | C )↔ T (B | C )) ∧(A ≤i
P B ∨ A ≈iP B)) (Relative tolerance)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 10 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
P 6≤iP Q ≡ ¬(P ≤i
P Q) (Not as preferred)
P <iP Q ≡ (P ≤i
P Q ∧ Q 6≤iP P) (Strictly preferred)
P ≈iP Q ≡ (P ≤i
P Q ∧ Q ≤iP P)
∨ (P 6≤iP Q ∧ Q 6≤i
P P) (Equally preferred)
A ≤iT (C) B ≡ (T (A | C ) ∧ ¬T (B | C )) ∨
((T (A | C )↔ T (B | C )) ∧(A ≤i
P B ∨ A ≈iP B)) (Relative tolerance)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 10 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
P 6≤iP Q ≡ ¬(P ≤i
P Q) (Not as preferred)
P <iP Q ≡ (P ≤i
P Q ∧ Q 6≤iP P) (Strictly preferred)
P ≈iP Q ≡ (P ≤i
P Q ∧ Q ≤iP P)
∨ (P 6≤iP Q ∧ Q 6≤i
P P) (Equally preferred)
A ≤iT (C) B ≡ (T (A | C ) ∧ ¬T (B | C )) ∨
((T (A | C )↔ T (B | C )) ∧(A ≤i
P B ∨ A ≈iP B)) (Relative tolerance)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 10 / 22
A Logic for Qualitative Decision Theory Abbreviations
Abbreviations
P 6≤iP Q ≡ ¬(P ≤i
P Q) (Not as preferred)
P <iP Q ≡ (P ≤i
P Q ∧ Q 6≤iP P) (Strictly preferred)
P ≈iP Q ≡ (P ≤i
P Q ∧ Q ≤iP P)
∨ (P 6≤iP Q ∧ Q 6≤i
P P) (Equally preferred)
A ≤iT (C) B ≡ (T (A | C ) ∧ ¬T (B | C )) ∨
((T (A | C )↔ T (B | C )) ∧(A ≤i
P B ∨ A ≈iP B)) (Relative tolerance)
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 10 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences
Model
MC = 〈M,D,O,C ,B 〉,
where
M is an extended QDT-model as defined above,
D is for each agent the set of desires,
O is the set of obligations,
C is for each agent the set of controllable propositions,
B is the belief base for each agent.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 11 / 22
Modelling influences and consequences Expected consequence
Expected consequence
Define the the set of potential consequences C ′(i) for an agent i asfollows:
if ϕ ∈ C (i) then ϕ,¬ϕ ∈ C ′(i)
The expected consequence(s) of bringing about ϕ is then:
ECi (ϕ) =∧
Cϕ for all Cϕ ∈ {Cϕ | (B(i) ∧ ϕ⇒ Cϕ) where Cϕ ∈ C ′(i)}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 12 / 22
Modelling influences and consequences Making a decision
Making a decision
The set of influences: I(i) = D(i) ∪ O
The set of best influences:
Dec(i) = {A | A ∈ I(i), andfor all B ∈ I(i),B 6= A, eitherA <i
P B, orA ≈i
P B and EC (A) ≤iT (A∨B) EC (B)}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 13 / 22
Modelling influences and consequences Example
Example
D(a) = {¬work}O = {work}
Alice’s preferences
I (¬snow | >)
I (¬work | snow)
SW
SW
SW SW
Expectation
> ⇒ work
snow ⇒ ¬work
SW
SW
SW
SW
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 14 / 22
Modelling influences and consequences Example
Example
D(a) = {¬work}O = {work}
Alice’s preferences
I (¬snow | >)
I (¬work | snow)
SW
SW
SW SW
Expectation
> ⇒ work
snow ⇒ ¬work
SW
SW
SW
SW
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 14 / 22
Modelling influences and consequences Example
BB = {¬snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {work ,¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 15 / 22
Modelling influences and consequences Example
BB = {¬snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {work ,¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 15 / 22
Modelling influences and consequences Example
BB = {¬snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {work ,¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 15 / 22
Modelling influences and consequences Example
BB = {snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 16 / 22
Modelling influences and consequences Example
BB = {snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 16 / 22
Modelling influences and consequences Example
BB = {snow}
Alice’s preferences
SW
SW
SW SW
Expectation
SW
SW
SW
SW
Dec(a) = {¬work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 16 / 22
Modelling influences and consequences Example — Revised
Example — Revised
D(a) = {¬work}, O = {work}
Alice’s preferences
I (¬snow | >)
I (¬work | snow)
I (¬fired | >)
FSW FSW FSW FSW
FSW
FSW
FSW FSW
Expectation
> ⇒ work
snow ⇒ ¬work
¬work ∧ ¬snow ⇒ fired
FSW FSW FSW FSW
FSW
FSW
FSW
FSW
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 17 / 22
Modelling influences and consequences Example — Revised
Example — Revised
D(a) = {¬work}, O = {work}
Alice’s preferences
I (¬snow | >)
I (¬work | snow)
I (¬fired | >)
FSW FSW FSW FSW
FSW
FSW
FSW FSW
Expectation
> ⇒ work
snow ⇒ ¬work
¬work ∧ ¬snow ⇒ fired
FSW FSW FSW FSW
FSW
FSW
FSW
FSW
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 17 / 22
Modelling influences and consequences Example — Revised
BB = {¬snow}
Alice’s preferences
FSW FSW FSW FSW
FSW
FSW
FSW FSW
Expectation
FSW FSW FSW FSW
FSW
FSW
FSW
FSW
Dec(a) = {work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 18 / 22
Modelling influences and consequences Example — Revised
BB = {¬snow}
Alice’s preferences
FSW FSW FSW FSW
FSW
FSW
FSW FSW
Expectation
FSW FSW FSW FSW
FSW
FSW
FSW
FSW
Dec(a) = {work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 18 / 22
Modelling influences and consequences Example — Revised
BB = {¬snow}
Alice’s preferences
FSW FSW FSW FSW
FSW
FSW
FSW FSW
Expectation
FSW FSW FSW FSW
FSW
FSW
FSW
FSW
Dec(a) = {work}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 18 / 22
Modelling influences and consequences Example — Revised
“Social” or “Selfish”?
In some cases the agent violates its obligation.
In other cases it ignores its desire.
E.g. leaving early does not have the consequence of getting fired.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 19 / 22
Modelling influences and consequences Example — Revised
“Social” or “Selfish”?
In some cases the agent violates its obligation.
In other cases it ignores its desire.
E.g. leaving early does not have the consequence of getting fired.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 19 / 22
Modelling influences and consequences Example — Revised
“Social” or “Selfish”?
In some cases the agent violates its obligation.
In other cases it ignores its desire.
E.g. leaving early does not have the consequence of getting fired.
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 19 / 22
A prototype
A prototype in Prolog
?- decide([~s], Dec).
Dec = [w].
?- decide([s], Dec).
Dec = [~w].
Usable in the GOAL agent programming language.
main module {
knowledge {
#import "decision.pl"
}
...
}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 20 / 22
A prototype
A prototype in Prolog
?- decide([~s], Dec).
Dec = [w].
?- decide([s], Dec).
Dec = [~w].
Usable in the GOAL agent programming language.
main module {
knowledge {
#import "decision.pl"
}
...
}
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 20 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Conclusion
Conclusion
Issues in agent-centered multi-agent systems
Organizations to the rescue
New conflicts arise
Resolved using expected consequences
No labeling of ‘social’ or ‘selfish’ agents
Prototype
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 21 / 22
Questions?
Andreas Schmidt Jensen AMAPS2012 November 29, 2012 22 / 22