A Combinatorial Auction for Collaborative Planning

When rational, utility-maximizing agents encounter an opportunity to collaborate on a group activity they must determine whether to commit to that activity. We refer to this problem as the initial-commitment decision problem (ICDP). The paper describes a mechanism that agents may use to solve the ICDP. The mechanism is based on a combinatorial auction in which agents bid on sets of roles in the group activity, each role comprising constituent subtasks that must be done by the same agent. Each bid may specify constraints on the execution times of the subtasks it covers. This mechanism permits agents to keep most details of their individual schedules of prior commitments private. The paper reports the results of several experiments testing the performance of the mechanism. These results demonstrate a significant improvement in performance when constituent subtasks are grouped into roles. They also show that as the number of time constraints in bids increases, the probability that there is a solution decreases, the cost of an optimal solution (if one exists) increases, and the time required to find an optimal solution (if one exists) decreases. The paper also describes several strategies that agents might employ when using this mechanism.Engineering and Applied Science

The Dynamics of Intentions in Collaborative Intentionality

An adequate formulation of collective intentionality is crucial for understanding group activity and for modeling the mental state of participants in such activities. Although work on collective intentionality in philosophy, artificial intelligence, and cognitive science has many points of agreement, several key issues remain under debate. This paper argues that the dynamics of intention – in particular, the inter-related processes of plan-related group decision making and intention updating – play crucial roles in an explanation of collective intentionality. Furthermore, it is in these dynamic aspects that coordinated group activity differs most from individual activity. The paper specifies a model of the dynamics of agent intentions in the context of collaborative activity. Its integrated treatment of group decision making and coordinated updating of group-related intentions fills an important gap in prior accounts of collective intentionality, thus helping to resolve a long-standing debate about the nature of intentions in group activity. The paper also defines an architecture for collaboration-capable computer agents that satisfies the constraints of the model and is a natural extension of the standard architecture for resource-bounded agents operating as individuals. The new architecture is both more principled and more complete than prior architectures for collaborative multi-agent systems.Engineering and Applied Science

Foundations of Dispatchability for Simple Temporal Networks with Uncertainty

Simple Temporal Networks (STNs) are a widely used formalism for representing and reasoning about tem- poral constraints on activities. The dispatchability of an STN was originally defined as a guarantee that a specific real-time execution algorithm would necessarily satisfy all of the STN’s constraints while preserv- ing maximum flexibility but requiring minimal computation. A Simple Temporal Network with Uncertainty (STNU) augments an STN to accommodate actions with uncertain durations. However, the dispatchability of an STNU was defined differently: in terms of the dispatchability of its so-called STN projections. It was then argued informally that this definition provided a similar real-time execution guarantee, but without specifying the execution algorithm. This paper formally defines a real-time execution algorithm for STNUs that similarly preserves maximum flexibility while requiring minimal computation. It then proves that an STNU is dispatch- able if and only if every run of that real-time execution algorithm necessarily satisfies the STNU’s constraints no matter how the uncertain durations play out. By formally connecting STNU dispatchability to an explicit real-time execution algorithm, the paper fills in important elements of the foundations of the dispatchability of STNUs

A note on speeding up DC-checking for STNUs

A Simple Temporal Network with Uncertainty (STNU) includes real-valued variables, called time-points; binary difference constraints on those time-points; and contingent links that represent actions with uncertain durations. The most important property of an STNU is called dynamic controllability (DC); and algorithms for checking this property are called DC-checking algorithms. The DC-checking algorithm for STNUs with the best worst-case time-complexity is the RUL$^-$ algorithm due to Cairo, Hunsberger and Rizzi. Its complexity is $O(mn + k^2n + knlog n)$, where $n$ is the number of time-points, $m$ is the number of constraints (equivalently, the number of edges in the STNU graph), and $k$ is the number of contingent links. It is expected that this worst-case complexity cannot be improved upon. However, this paper provides a new implementation of the algorithm that improves its performance in practice by an order of magnitude, as demonstrated by a thorough empirical evaluation

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops. This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms

Sound-and-Complete Algorithms for Checking the Dynamic Controllability of Conditional Simple Temporal Networks with Uncertainty

A Conditional Simple Temporal Network with Uncertainty (CSTNU) is a data structure for representing and reasoning about time. CSTNUs incorporate observation time-points from Conditional Simple Temporal Networks (CSTNs) and contingent links from Simple Temporal Networks with Uncertainty (STNUs). A CSTNU is dynamically controllable (DC) if there exists a strategy for executing its time-points that guarantees the satisfaction of all relevant constraints no matter how the uncertainty associated with its observation time-points and contingent links is resolved in real time. This paper presents the first sound-and-complete DC-checking algorithms for CSTNUs that are based on the propagation of labeled constraints and demonstrates their practicality

Speeding Up the RUL¯ Dynamic-Controllability-Checking Algorithm for Simple Temporal Networks with Uncertainty

A Simple Temporal Network with Uncertainty (STNU) in- cludes real-valued variables, called time-points; binary differ- ence constraints on those time-points; and contingent links that represent actions with uncertain durations. STNUs have been used for robot control, web-service composition, and business processes. The most important property of an STNU is called dynamic controllability (DC); and algorithms for checking this property are called DC-checking algorithms. The DC- checking algorithm for STNUs with the best worst-case time- complexity is the RUL− algorithm due to Cairo, Hunsberger and Rizzi. Its complexity is O(mn + k2n + kn log n), where n is the number of time-points, m is the number of constraints, and k is the number of contingent links. It is expected that this worst-case complexity cannot be improved upon. However, this paper provides a new algorithm, called RUL2021, that improves its performance in practice by an order of magnitude, as demonstrated by a thorough empirical evaluation

Propagating Piecewise-Linear Weights in Temporal Networks

This paper presents a novel technique using piecewise-linear functions (PLFs) as weights on edges in the graphs of two kinds of temporal networks to solve several previously open problems. Generalizing constraint-propagation rules to accom- modate PLF weights requires implementing a small handful of functions. Most problems are solved by inserting one or more edges with an initial weight of \u3b4 (a variable), then using the modified rules to propagate the PLF weights. For one kind of network, a new set of propagation rules is introduced to avoid a non-termination issue that arises when propagating PLF weights. The paper also presents two new results for determining the tightest horizon that can be imposed while preserving a network\u2019s dynamic consistency/controllability

Group Decision Making and Temporal Reasoning

The more capable and autonomous computer systems become, the more important it is for them to be able to act collaboratively, whether in groups consisting solely of other computers or in heterogeneous groups of computers and people. To act collaboratively requires that systems have effective group decision-making capabilities. This thesis makes four important contributions to the design of group decision-making mechanisms and algorithms for deploying them in collaborative, multi-agent systems. First, it provides an abstract framework for the specification of group decision-making mechanisms that computer agents can use to coordinate their planning activity when collaborating with other agents. Second, it specifies a combinatorial auction-based mechanism that computer agents can use to help them decide, both individually and collectively, whether to engage in a collaborative activity. Third, it extends the theory of Simple Temporal Networks by providing a rigorous theoretical analysis of an important family of temporal reasoning problems. Fourth, it provides sound, complete and polynomial-time algorithms for solving those temporal reasoning problems and specifies the use of such algorithms by agents participating in the auction-based mechanism.Engineering and Applied Science

Reducing Dynamic-Consistency (DC) Checking for Conditional Simple Temporal Networks (CSTNs) with Bounded Reaction Times to Standard DC Checking for CSTNs

Recent work on Conditional Simple Temporal Networks (CSTNs) has introduced the problem of checking the dynamic consistency (DC) property for the case where the reaction of an execution strategy to observations is bounded below by some fixed \u3b5 > 0. This paper shows how the \u3b5-DC-checking problem can be easily reduced to the standard DC-checking problem for CSTNs. Given any CSTN S with k observation time-points, the paper defines a new CSTN S0 that is the same as S, except that it includes k new observation time-points. For each observation time-point P? in S that observes some proposition p, the time-point P? in S0 is demoted from an observation time-point to an ordinary time-point; and the job of observing p is taken over by a new observation time-point P0? that is constrained to occur exactly \u3b5 after P?. The paper proves that S is \u3b5-DC if and only if S0 is DC; and shows that the application of the \u3b5-DC- checking constraint-propagation rules to S is equivalent to the application of the corresponding DC-checking constraint-propagation rules to S0. Two versions of these results are presented, depending on whether a dynamic strategy for S0 can react instantaneously or only after some arbitrarily small, positive delay. Finally, the paper demonstrates empirically that the performance of building S0 and DC-checking it is even better than \u3b5-DC-checking the original instance S