Coordinated constraint relaxation using a distributed agent protocol

The interactions among agents in a multi-agent system for coordinating a distributed, problem solving task can be complex, as the distinct sub-problems of the individual agents are interdependent. A distributed protocol provides the necessary framework for specifying these interactions. In a model of interactions where the agents' social norms are expressed as the message passing behaviours associated with roles, the dependencies among agents can be specified as constraints. The constraints are associated with roles to be adopted by agents as dictated by the protocol. These constraints are commonly handled using a conventional constraint solving system that only allows two satisfactory states to be achieved - completely satisfied or failed. Agent interactions then become brittle as the occurrence of an over-constrained state can cause the interaction between agents to break prematurely, even though the interacting agents could, in principle, reach an agreement. Assuming that the agents are capable of relaxing their individual constraints to reach a common goal, the main issue addressed by this thesis is how the agents could communicate and coordinate the constraint relaxation process. The interaction mechanism for this is obtained by reinterpreting a technique borrowed from the constraint satisfaction field, deployed and computed at the protocol level.The foundations of this work are the Lightweight Coordination Calculus (LCC) and the distributed partial Constraint Satisfaction Problem (CSP). LCC is a distributed interaction protocol language, based on process calculus, for specifying and executing agents' social norms in a multi-agent system. Distributed partial CSP is an extension of partial CSP, a means for managing the relaxation of distributed, over-constrained, CSPs. The research presented in this thesis concerns how distributed partial CSP technique, used to address over-constrained problems in the constraint satisfaction field, could be adopted and integrated within the LCC to obtain a more flexible means for constraint handling during agent interactions. The approach is evaluated against a set of overconstrained Multi-agent Agreement Problems (MAPs) with different levels of hardness. Not only does this thesis explore a flexible and novel approach for handling constraints during the interactions of heterogeneous and autonomous agents participating in a problem solving task, but it is also grounded in a practical implementation

Data-parallel concurrent constraint programming.

by Bo-ming Tong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 104-[110]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Concurrent Constraint Programming --- p.2Chapter 1.2 --- Finite Domain Constraints --- p.3Chapter 2 --- The Firebird Language --- p.5Chapter 2.1 --- Finite Domain Constraints --- p.6Chapter 2.2 --- The Firebird Computation Model --- p.6Chapter 2.3 --- Miscellaneous Features --- p.7Chapter 2.4 --- Clause-Based N on determinism --- p.9Chapter 2.5 --- Programming Examples --- p.10Chapter 2.5.1 --- Magic Series --- p.10Chapter 2.5.2 --- Weak Queens --- p.14Chapter 3 --- Operational Semantics --- p.15Chapter 3.1 --- The Firebird Computation Model --- p.16Chapter 3.2 --- The Firebird Commit Law --- p.17Chapter 3.3 --- Derivation --- p.17Chapter 3.4 --- Correctness of Firebird Computation Model --- p.18Chapter 4 --- Exploitation of Data-Parallelism in Firebird --- p.24Chapter 4.1 --- An Illustrative Example --- p.25Chapter 4.2 --- Mapping Partitions to Processor Elements --- p.26Chapter 4.3 --- Masks --- p.27Chapter 4.4 --- Control Strategy --- p.27Chapter 4.4.1 --- A Control Strategy Suitable for Linear Equations --- p.28Chapter 5 --- Data-Parallel Abstract Machine --- p.30Chapter 5.1 --- Basic DPAM --- p.31Chapter 5.1.1 --- Hardware Requirements --- p.31Chapter 5.1.2 --- Procedure Calling Convention And Process Creation --- p.32Chapter 5.1.3 --- Memory Model --- p.34Chapter 5.1.4 --- Registers --- p.41Chapter 5.1.5 --- Process Management --- p.41Chapter 5.1.6 --- Unification --- p.49Chapter 5.1.7 --- Variable Table --- p.49Chapter 5.2 --- DPAM with Backtracking --- p.50Chapter 5.2.1 --- Choice Point --- p.52Chapter 5.2.2 --- Trailing --- p.52Chapter 5.2.3 --- Recovering the Process Queues --- p.57Chapter 6 --- Implementation --- p.58Chapter 6.1 --- The DECmpp Massively Parallel Computer --- p.58Chapter 6.2 --- Implementation Overview --- p.59Chapter 6.3 --- Constraints --- p.60Chapter 6.3.1 --- Breaking Down Equality Constraints --- p.61Chapter 6.3.2 --- Processing the Constraint 'As Is' --- p.62Chapter 6.4 --- The Wide-Tag Architecture --- p.63Chapter 6.5 --- Register Window --- p.64Chapter 6.6 --- Dereferencing --- p.65Chapter 6.7 --- Output --- p.66Chapter 6.7.1 --- Collecting the Solutions --- p.66Chapter 6.7.2 --- Decoding the solution --- p.68Chapter 7 --- Performance --- p.69Chapter 7.1 --- Uniprocessor Performance --- p.71Chapter 7.2 --- Solitary Mode --- p.73Chapter 7.3 --- Bit Vectors of Domain Variables --- p.75Chapter 7.4 --- Heap Consumption of the Heap Frame Scheme --- p.77Chapter 7.5 --- Eager Nondeterministic Derivation vs Lazy Nondeterministic Deriva- tion --- p.78Chapter 7.6 --- Priority Scheduling --- p.79Chapter 7.7 --- Execution Profile --- p.80Chapter 7.8 --- Effect of the Number of Processor Elements on Performance --- p.82Chapter 7.9 --- Change of the Degree of Parallelism During Execution --- p.84Chapter 8 --- Related Work --- p.88Chapter 8.1 --- Vectorization of Prolog --- p.89Chapter 8.2 --- Parallel Clause Matching --- p.90Chapter 8.3 --- Parallel Interpreter --- p.90Chapter 8.4 --- Bounded Quantifications --- p.91Chapter 8.5 --- SIMD MultiLog --- p.91Chapter 9 --- Conclusion --- p.93Chapter 9.1 --- Limitations --- p.94Chapter 9.1.1 --- Data-Parallel Firebird is Specialized --- p.94Chapter 9.1.2 --- Limitations of the Implementation Scheme --- p.95Chapter 9.2 --- Future Work --- p.95Chapter 9.2.1 --- Extending Firebird --- p.95Chapter 9.2.2 --- Improvements Specific to DECmpp --- p.99Chapter 9.2.3 --- Labeling --- p.100Chapter 9.2.4 --- Parallel Domain Consistency --- p.101Chapter 9.2.5 --- Branch and Bound Algorithm --- p.102Chapter 9.2.6 --- Other Possible Future Work --- p.102Bibliography --- p.10

Box constraint collections for adhoc constraints.

Cheng Chi Kan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 101-105).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Background --- p.4Chapter 2.1 --- Propagation Based Constraint Solving --- p.4Chapter 2.1.1 --- "Valuations, Domains and Constraints" --- p.4Chapter 2.1.2 --- Solving a CSP --- p.6Chapter 2.1.3 --- Propagators --- p.7Chapter 2.1.4 --- Domain Consistency --- p.8Chapter 2.1.5 --- Bounds Consistency --- p.9Chapter 2.1.6 --- Propagation-based Backtracking Search --- p.10Chapter 2.2 --- Disjunction --- p.12Chapter 2.2.1 --- Speculative --- p.12Chapter 2.2.2 --- Cardinality --- p.12Chapter 2.2.3 --- Constructive Disjunction --- p.13Chapter 3 --- Box Constraint Collections --- p.15Chapter 3.1 --- Box Constraint Collections --- p.15Chapter 3.2 --- Separable Constraints --- p.17Chapter 4 --- Building Box Constraint Collections --- p.22Chapter 4.1 --- The bccFinder Algorithm --- p.22Chapter 4.2 --- Heuristics for the bccFinder Algorithm --- p.30Chapter 4.2.1 --- The Order of Box Expansion --- p.30Chapter 4.2.2 --- The Conditions of Box Expansion --- p.35Chapter 5 --- Compiling BCCs into Indexicals --- p.37Chapter 5.1 --- Indexicals --- p.37Chapter 5.2 --- Basic Compilation --- p.45Chapter 5.3 --- Optimizing Compilation --- p.49Chapter 5.3.1 --- Subsumption Indexicals --- p.49Chapter 5.3.2 --- Union Indexicals --- p.50Chapter 5.4 --- Hybrid Approach --- p.71Chapter 6 --- Experiments --- p.76Chapter 7 --- Related Work --- p.93Chapter 8 --- Concluding Remarks --- p.98Chapter 8.1 --- Contributions --- p.98Chapter 8.2 --- Future Work --- p.9

Entailment of finite domain constraints

P. Van Hentenryck, editor. </p

Entailment of finite domain constraints

Using a glass-box theory of finite domain constraints, FD, we show how the entailment of user-defined constraints can be expressed by antimonotone FD constraints. We also provide an algorithm for checking the entailment and consistency of FD constraints. FD is shown to be expressive enough to allow the definition of arithmetical constraints, as well as nontrivial symbolic constraints, that are normally built in to CLP systems. In particular, we use conditional FD constraints, which exploit entailment checking, to define symbolic constraints. Thus, we claim that a glass-box system such as FD is expressive enough to capture the essence of finite domain constraint programming