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E-raamat: Teamwork in Multi-Agent Systems: A Formal Approach

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(University of Groningen), (Warsaw University)
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What makes teamwork tick? Cooperation matters, in daily life and in complex applications. After all, many tasks need more than a single agent to be effectively performed. Therefore, teamwork rules! Teams are social groups of agents dedicated to the fulfilment of particular persistent tasks. In modern multiagent environments, heterogeneous teams often consist of autonomous software agents, various types of robots and human beings. Teamwork in Multi-agent Systems: A Formal Approach explains teamwork rules in terms of agents attitudes and their complex interplay. It provides the first comprehensive logical theory, TeamLog, underpinning teamwork in dynamic environments. The authors justify design choices by showing TeamLog in action. The book guides the reader through a fascinating discussion of issues essential for teamwork to be successful: What is teamwork, and how can a logical view of it help in designing teams of agents? What is the role of agents awareness in an uncertain, dynamic environment? How does collective intention constitute a team? How are plan-based collective commitments related to team action? How can one tune collective commitment to the teams organizational structure and its communication abilities? What are the methodological underpinnings for teamwork in a dynamic environment? How does a team and its attitudes adjust to changing circumstances? How do collective intentions and collective commitments arise through dialogue? What is the computational complexity of TeamLog? How can one make TeamLog efficient in applications? This book is an invaluable resource for researchers and graduate students in computer science and artificial intelligence as well as for developers of multi-agent systems. Students and researchers in organizational science, in particular those investigating teamwork, will also find this book insightful.  Since the authors made an effort to introduce TeamLog as a conceptual model of teamwork, understanding most of the book requires solely a basic logical background.

Arvustused

 "In sum, the prospective contribution of this book is expected to have an impact on a number of aspects that revolve around the modeling and simulation of teamwork, both from theory and application issues. Therefore, I recommend it to researchers in computer sciences, organization theory and social psychology who are interested in collective thinking, collaborative work and organizational phenomena, as well as to practitioners who deal everyday with the challenge of managing teams in many different settings." (Journal of Artificial Societies and Social Simulation, February 2011)

About the Authors xiii
Foreword xv
Preface xvii
1 Teamwork in Multi-Agent Environments
1(10)
1.1 Autonomous Agents
1(1)
1.2 Multi-Agent Environments as a Pinnacle of Interdisciplinarity
2(1)
1.3 Why Teams of Agents?
2(1)
1.4 The Many Flavors of Cooperation
3(1)
1.5 Agents with Beliefs, Goals and Intentions
4(1)
1.6 From Individuals to Groups
4(1)
1.7 Group Attitudes
5(1)
1.8 A Logical View on Teamwork: TeamLog
5(1)
1.9 Teamwork in Times of Change
6(1)
1.10 Our Agents are Planners
7(1)
1.11 Temporal or Dynamic?
8(1)
1.12 From Real-World Data to Teamwork
9(1)
1.13 How Complex are Models of Teamwork?
10(1)
2 Beliefs in Groups
11(18)
2.1 Awareness is a Vital Ingredient of Teamwork
11(1)
2.2 Perception and Beliefs
12(1)
2.3 Language and Models for Beliefs
13(1)
2.3.1 The Logical Language for Beliefs
13(1)
2.3.2 Kripke Models for Beliefs
14(1)
2.4 Axioms for Beliefs
14(4)
2.4.1 Individual Beliefs
15(1)
2.4.2 From General to Common Belief
16(2)
2.5 Axioms for Knowledge
18(2)
2.6 Relations between Knowledge and Belief
20(1)
2.7 Levels of Agents' Awareness
21(8)
2.7.1 Intra-Personal Awareness
21(2)
2.7.2 Inter-Personal Awareness
23(1)
2.7.3 Group Awareness
24(1)
2.7.4 Degrees of Beliefs in a Group
25(4)
3 Collective Intentions
29(26)
3.1 Intentions in Practical Reasoning
29(3)
3.1.1 Moving Intentions to the Collective Level
31(1)
3.2 Language and Models for Goals and Intentions
32(1)
3.2.1 The Logical Language
32(1)
3.2.2 Kripke Models
32(1)
3.3 Goals and Intentions of Individual Agents
33(3)
3.3.1 Interdependencies between Attitudes
34(2)
3.4 Collective Intention Constitutes a Group
36(1)
3.5 Definitions of Mutual and Collective Intentions
37(3)
3.5.1 Some Examples
39(1)
3.5.2 Collective Intentions Allow Collective Introspection
40(1)
3.6 Collective Intention as an Infinitary Concept
40(3)
3.6.1 Mutual Intention is Created in a Finite Number of Steps
41(1)
3.6.2 Comparison with the One-Level Definition
41(1)
3.6.3 Comparison with the Two-Level Definition
42(1)
3.6.4 Can the Infinitary Concept be Replaced by a Finite Approximation?
43(1)
3.7 Alternative Definitions
43(2)
3.7.1 Rescue Situations
43(2)
3.7.2 Tuning Group Intentions to the Environment
45(1)
3.8 The Logic of Mutual Intention TEAMLOGmint is Complete
45(7)
3.9 Related Approaches to Intentions in a Group
52(3)
3.9.1 What Next?
53(2)
4 A Tuning Machine for Collective Commitments
55(26)
4.1 Collective Commitment
55(2)
4.1.1 Gradations of Teamwork
55(1)
4.1.2 Collective Commitment Triggers Team Action
56(1)
4.1.3 A Tuning Mechanism
56(1)
4.2 The Language and Kripke Semantics
57(3)
4.2.1 Language
57(2)
4.2.2 Kripke Models
59(1)
4.3 Building Collective Commitments
60(3)
4.3.1 Social Plans
60(1)
4.3.2 Social Commitments
61(1)
4.3.3 Deontic Aspects of Social Commitments
62(1)
4.3.4 Commitment Strategies
63(1)
4.4 Tuning Collective Commitments
63(6)
4.4.1 Why Collective Commitment?
63(2)
4.4.2 General Schema of Collective Commitment
65(2)
4.4.3 A Paradigmatic Group Commitment
67(2)
4.5 Different Notions of Collective Commitment
69(3)
4.5.1 Robust Collective Commitment
69(1)
4.5.2 Strong Collective Commitment
70(1)
4.5.3 Weak Collective Commitment
70(1)
4.5.4 Team Commitment
71(1)
4.5.5 Distributed Commitment
71(1)
4.5.6 Awareness of Group Commitment
72(1)
4.6 Topologies and Group Commitments
72(6)
4.6.1 Robust Commitments with a Single Initiator under Infallible Communication
73(1)
4.6.2 Star Topology with a Single Initiator under Restricted Communication
74(1)
4.6.3 Ring Topology with a Single Initiator
75(2)
4.6.4 A Hierarchical Group: Trees of Shallow Depth
77(1)
4.7 Summing up TeamLog: The Static Part of the Story
78(3)
4.7.1 Comparison
79(1)
4.7.2 Moving Towards a Dynamic View on Teamwork
79(2)
5 Reconfiguration in a Dynamic Environment
81(18)
5.1 Dealing with Dynamics
81(2)
5.1.1 Collective Commitments in Changing Circumstances
82(1)
5.1.2 Three Steps that Lead to Team Action
82(1)
5.2 The Four Stages of Teamwork
83(3)
5.2.1 Potential Recognition
83(2)
5.2.2 Team Formation
85(1)
5.2.3 Plan Generation
85(1)
5.2.4 Team Action
86(1)
5.3 The Reconfiguration Method
86(5)
5.3.1 Continuity and Conservativity
88(1)
5.3.2 Reconfiguration Algorithm = Teamwork in Action
88(1)
5.3.3 Cycling through Reconfiguration
89(2)
5.3.4 Complexity of the Algorithm
91(1)
5.4 Case Study of Teamwork: Theorem Proving
91(8)
5.4.1 Potential Recognition
92(1)
5.4.2 Team Formation
93(1)
5.4.3 Plan Generation
93(1)
5.4.4 A Social Plan for Proving the Theorem
94(1)
5.4.5 A Collective Commitment to Prove the Theorem
94(1)
5.4.6 Team Action
95(4)
6 The Evolution of Commitments during Reconfiguration
99(28)
6.1 A Formal View on Commitment Change
99(2)
6.1.1 Temporal versus Dynamic Logic
100(1)
6.2 Individual Actions and Social Plan Expressions
101(3)
6.2.1 The Logical Language of TeamLog dyn
101(3)
6.3 Kripke Models
104(4)
6.3.1 Axioms for Actions and Social Plans
106(2)
6.4 Dynamic Description of Teamwork
108(7)
6.4.1 Operationalizing the Stages of Teamwork
108(7)
6.5 Evolution of Commitments During Reconfiguration
115(7)
6.5.1 Commitment Change: Zooming Out
115(1)
6.5.2 Commitment Change: Case by Case
116(6)
6.5.3 Persistence of Collective Intention
122(1)
6.6 TeamLog Summary
122(5)
7 A Case Study in Environmental Disaster Management
127(12)
7.1 A Bridge from Theory to Practice
127(1)
7.2 The Case Study: Ecological Disasters
128(2)
7.2.1 Starting Point: the Agents
129(1)
7.2.2 Cooperation between Subteams
129(1)
7.2.3 A Bird's-Eye View on Cases
130(1)
7.3 Global Plans
130(4)
7.3.1 The Global Social Plan (Cleanup)
130(1)
7.3.2 The Social Plan (SR)
131(1)
7.3.3 The Social Plan (E)
131(1)
7.3.4 The Social Plan (D1 R)
132(1)
7.3.5 The Social Plan (D1 N)
132(1)
7.3.6 The Social Plan (D2 R)
133(1)
7.3.7 The Social Plan (D2 N)
133(1)
7.4 Adjusting the TeamLog Definitions to the Case Study
134(4)
7.4.1 Projections
134(1)
7.4.2 Organization Structure: Who is Socially Committed to Whom?
135(1)
7.4.3 Minimal Levels of Group Intention and Awareness
135(3)
7.4.4 Complexity of the Language Without Collective Attitudes
138(1)
7.5 Conclusion
138(1)
8 Dialogue in Teamwork
139(30)
8.1 Dialogue as a Synthesis of Three Formalisms
139(1)
8.2 Dialogue Theory and Dialogue Types
140(3)
8.2.1 Persuasion
141(1)
8.2.2 Negotiation
142(1)
8.2.3 Inquiry
142(1)
8.2.4 Deliberation
143(1)
8.2.5 Information Seeking
143(1)
8.3 Zooming in on Vital Aspects of Dialogue
143(4)
8.3.1 Trust in Dialogues
143(1)
8.3.2 Selected Speech Acts
144(1)
8.3.3 Rigorous Persuasion
145(2)
8.4 Information Seeking During Potential Recognition
147(3)
8.5 Persuasion During Team Formation
150(7)
8.5.1 Creating Collective Intention
150(1)
8.5.2 Agents Persuading One Another to Join the Team
151(1)
8.5.3 Speech Acts and their Consequences During Persuasion
152(2)
8.5.4 Announcing the Success of Team Formation
154(1)
8.5.5 Team Formation Through the Magnifying Glass
155(2)
8.6 Deliberation During Planning
157(9)
8.6.1 Stages of Deliberation: Who Says What and with Which Effect?
157(3)
8.6.2 The Three Steps of Planning
160(1)
8.6.3 Task Division under the Magnifying Glass
161(2)
8.6.4 Action Allocation Under the Magnifying Glass
163(3)
8.7 Dialogues During Team Action
166(2)
8.7.1 Communication Supports Reconfiguration
167(1)
8.8 Discussion
168(1)
9 Complexity of Teamlog
169(28)
9.1 Computational Complexity
169(4)
9.1.1 Satisfiability, Validity and Model Checking
170(2)
9.1.2 Combination May Lead to Explosion
172(1)
9.2 Logical Background
173(3)
9.2.1 The Language
173(1)
9.2.2 Semantics Based on Kripke Models
174(1)
9.2.3 Axiom Systems for Individual and Collective Attitudes
175(1)
9.3 Complexity of TeamLog ind
176(7)
9.3.1 The Algorithm for Satisfiability of TeamLog ind
179(3)
9.3.2 Effect of Bounding Modal Depth for TeamLog ind
182(1)
9.3.3 Effect of Bounding the Number of Propositional Atoms for TeamLog ind
183(1)
9.4 Complexity of the System TeamLog
183(11)
9.4.1 Effect of Bounding Modal Depth for TeamLog
186(4)
9.4.2 Effect of Bounding the Number of Propositional Atoms for TeamLog
190(1)
9.4.3 Effect of Restricting the Modal Context for TeamLog
191(3)
9.5 Discussion and Conclusions
194(3)
A Appendix A
197(8)
A.1 Axiom Systems
197(4)
A.1.1 Axioms for Individual and Collective Attitudes
197(1)
A.1.2 Axioms for Social Commitments
198(1)
A.1.3 Tuning Schemes for Social and Collective Attitudes
199(1)
A.1.4 Axioms for Exemplary Collective Commitments
199(2)
A.1.5 Axioms and Rules for Dynamic Logic
201(1)
A.2 An Alternative Logical Framework for Dynamics of Teamwork: Computation Tree Logic
201(4)
A.2.1 Commitment Strategies
203(1)
A.2.2 The Blocking Case Formalized in the Temporal Language
204(1)
Bibliography 205(12)
Index 217
Barbara Dunin-Keplicz is the head of the Multi-agent Systems Group at the Institute of Informatics of the Warsaw University and at the Institute of Computer Science of the Polish Academy of Sciences (ICS PAS). She was awarded a M.Sc. in 1976 from Warsaw University, PhD in 1990 from Jagiellonian University, and her habilitation in 2004 from ICS PAS. Dunin-Keplicz was a visiting research fellow at the Department of Computer Science at the Free University of Amsterdam, 1994-1997, and at the Department of Artificial Intelligence at the University of Groningen, from 1998. Her publications and research have been interdisciplinary. Starting from computational linguistics, reasoning about action and change, and formal theories of multiagent systems, including Agent Communication Languages, she is now focused on foundations of multiagent systems, especially on the theory of motivational attitudes in BDI systems. Rineke Verbrugge is Associate Professor at the University of Groningen at the Institute of Artificial Intelligence, to which she has been affiliated from 1997. Since 2002, she has been leader of the group Multi-agent Systems at the university, where her work focused on logics in artificial intelligence, specifically multi-agent systems, reasoning about others, and group reasoning. She received a M.Sc. (cum laude) in Mathematics in 1988 and a Ph.D. in Mathematics in 1993, both from the University of Amsterdam. Subsequently, she was post-doc at the Department of Logic at the Charles University in Prague (TEMPUS grant) and at the Department of Logic at the University of Gothenburg (NWO Talent stipend), as well as Visiting Assistant Professor at the Department of Linguistics and Philosophy at the Massachusetts Institute of Technology (MIT). From 1995 to 1997, she was Assistant Professor at the Department of Artificial Intelligence at the Vrije Universiteit Amsterdam.
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