Agreeing on Institutional Goals for Multi-Agent Societies D. Gaertner - PowerPoint PPT Presentation

Agreeing on Institutional Goals for Multi-Agent Societies D. Gaertner 1 , 2 , J.-A. Rodriguez 2 , F. Toni 1 1 Department of Computing, Imperial College, London, United Kingdom 2 Artificial Intelligence Research Institute, IIIA-CSIC, Bellaterra, Spain

Aims and Objectives 1 find agreement on a common set of goals 2 using tried-and-tested argumentation technology 3 sketch two approaches - central and distributed

Aims and Objectives 1 find agreement on a common set of goals 2 using tried-and-tested argumentation technology 3 sketch two approaches - central and distributed

Aims and Objectives 1 find agreement on a common set of goals 2 using tried-and-tested argumentation technology 3 sketch two approaches - central and distributed

Univ. of Texas paper Combining Job and Team Selection Heuristics Similarities: coalition formation large-scale, dynamic agent environment Differences: quantitative experiments vs. qualitative analysis builds on previous work - more mature and formal

King’s College paper Argumentation Heuristic for Normative Conflicts Similarities: use of argumentation technology to address COIN problems both needed for normative MAS - ours initially, theirs at run-time Differences: Dung’s Abstract Argumentation vs. Assumption-based Argumentation focus on individual agent vs. focus on collaboration

Argumentation Scenario Central Approach Distributed Approach 1 Assumption-based Argumentation Theory Practice 2 Scenario Goals Rules 3 Central Approach Construction Scenario - Revisited 4 Distributed Approach Construction Scenario - Revisited 5 Conclusions Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Formal Definition g An assumption-based framework is a tuple �L , R , A , � where ( L , R ) is a deductive system A ⊆ L is the set of candidate assumptions if c ∈ A , then there exists no inference rule of the form c ← c 1 , . . . , c n ∈ R is a (total) mapping from A into L , where α is the contrary of α Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Example Let �L , R , A , � be the assumption-based framework: L = { p , q , r , s , t , ¬ p , ¬ q , ¬ r , ¬ s , ¬ t } R consists of p ← q , r q ← s ¬ r ← t ¬ t ← A = { r , s , t } r = ¬ r , s = ¬ s , t = ¬ t { r , s } ⊢ p Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Semantics A set of assumptions is admissible iff it does not attack itself and counter-attacks every set of assumptions attacking it complete iff it is admissible and contains all assumptions it can defend ground iff it is minimally complete ideal iff it is admissible and contained in all maximally admissible sets Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach CaSAPI g system that determines acceptability and support of claims using assumption-based argumentation frameworks according to three semantics providing structured arguments and their inter-relationships Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Example in CaSAPI - Input Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Example in CaSAPI - Output Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Example in CaSAPI - Output Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Scenario Theory Central Approach Practice Distributed Approach Example in CaSAPI - GUI Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - General individual agents (A, B, C) in free and unregulated environments collaboration to fabricate motorbikes diverse goals: generic (e.g. good communication, sustainability of collaboration) business (e.g. good profits for all participants) domain (e.g. produce X bikes, few delivery delays) Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - Goals g 1 : to produce 100 motorbikes this week g 2 : to always clear the assembly line at the end of each day g 3 : to produce 100 sidecars this week g 4 : to improve/foster relations between the three collaborators g 5 : to make the institution sustainable/repeatable g 6 : to make Carles as the leader of this collaboration Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - Rules g 1 : to produce 100 motorbikes this week achievable in two ways: assuming sufficiently many spare parts are in stock a 3 rd party provider exists AND assuming outsourcing is acceptable g 1 ← a 1 g 1 ← r 1 , c 1 Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - Rules g 1 : to produce 100 motorbikes this week achievable in two ways: assuming sufficiently many spare parts are in stock a 3 rd party provider exists AND assuming outsourcing is acceptable g 1 ← a 1 g 1 ← r 1 , c 1 Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - Rules g 1 : to produce 100 motorbikes this week achievable in two ways: assuming sufficiently many spare parts are in stock a 3 rd party provider exists AND assuming outsourcing is acceptable g 1 ← a 1 , x 1 g 1 ← r 1 , c 1 , x 8 Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Basics Scenario Goals Central Approach Rules Distributed Approach Scenario - Rules IKB A IKB B IKB C g 1 ← a 1 , x 1 g 2 ← q 1 , x 5 g 1 ← r 1 , c 1 , x 8 g 2 ← p 1 , x 2 g 4 ← b 1 , q 2 , x 6 g 6 ← r 2 , x 9 g 3 ← p 2 , a 2 , x 3 g 5 ← q 3 , b 2 , x 7 g 3 ← a 3 , x 4 p 1 q 1 ← b 3 r 3 p 2 ← a 1 q 2 ¬ a 3 ← r 3 q 3 ¬ b 1 ← r 4 ¬ b 2 r 4 ← r 5 , c 2 r 5 ← c 3 SKB contains ¬ a 1 and r 1 ← r 3 Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Semantical Considerations A goal is “acceptable” if there exists an acceptable set of assumptions according to a given semantics. credulous semantics won’t work out of the skeptical semantics we choose the ground one Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Formal Definition For k agents, we construct the ABA framework as follows: R = SKB ∪ � n k =0 IKB k A = � n k =0 A k where A k are the assumptions of agent k including its applicability assumptions x ′ = { y | y = x i and i is an agent } Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Scenario - Revisited IKB A IKB B IKB C g 1 ← a 1 , x 1 g 2 ← q 1 , x 5 g 1 ← r 1 , c 1 , x 8 g 2 ← p 1 , x 2 g 4 ← b 1 , q 2 , x 6 g 6 ← r 2 , x 9 g 3 ← p 2 , a 2 , x 3 g 5 ← q 3 , b 2 , x 7 g 3 ← a 3 , x 4 p 1 q 1 ← b 3 r 3 p 2 ← a 1 q 2 ¬ a 3 ← r 3 q 3 ¬ b 1 ← r 4 ¬ b 2 r 4 ← r 5 , c 2 r 5 ← c 3 SKB contains ¬ a 1 and r 1 ← r 3 Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Pros and Cons PRO: computationally straight-forward CON: general issues of centralised systems performance issues / bottleneck / vulnerability to attacks / trust issues CON: privacy all knowledge must be shared (e.g. private business rules) Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Pros and Cons PRO: computationally straight-forward CON: general issues of centralised systems performance issues / bottleneck / vulnerability to attacks / trust issues CON: privacy all knowledge must be shared (e.g. private business rules) Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies

Argumentation Construction Scenario Scenario - Revisited Central Approach Discussion Distributed Approach Pros and Cons PRO: computationally straight-forward CON: general issues of centralised systems performance issues / bottleneck / vulnerability to attacks / trust issues CON: privacy all knowledge must be shared (e.g. private business rules) Gaertner et al. Agreeing on Institutional Goals for Multi-Agent Societies