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

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

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

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

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

A Streamlined Model of Conditional Simple Temporal Networks - Semantics and Equivalence Results

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network to include a new kind of time-points, called observation time-points. The execution of an observation time-point generates information in real time, specifically, the truth value of a propositional letter. In addition, time-points and temporal constraints may be labeled by conjunctions of (positive or negative) propositional letters. A CSTN is called dynamically consistent (DC) if there exists a dynamic strategy for executing its time-points such that no matter how the observations turn out during execution, the time-points whose labels are consistent with those observations have all been executed, and the constraints whose labels are consistent with those observations have all been satisfied. The strategy is dynamic in that its execution decisions may react to observations. The original formulation of CSTNs included propositional labels only on time-points, but the DC-checking algorithm was impractical because it was based on a conversion of the semantic constraints into an exponentially-sized Disjunctive Temporal Network. Later work added propositional labels to temporal constraints, and yielded a sound-and-complete propagation-based DC-checking algorithm, empirically demonstrated to be practical across a variety of CSTNs. This paper introduces a streamlined version of a CSTN in which propositional labels may appear on constraints, but not on time-points. This change simplifies the definition of the DC property, as well as the propagation rules for the DC-checking algorithm. It also simplifies the proofs of the soundness and completeness of those rules. This paper provides two translations from traditional CSTNs to streamlined CSTNs. Each translation preserves the DC property and, for any DC network, ensures that any dynamic execution strategy for that network can be extended to a strategy for its streamlined counterpart. Finally, this paper presents an empirical comparison of two versions of the DC-checking algorithm: the original version and a simplified version for streamlined CSTNs. The comparison is based on CSTN benchmarks from earlier work. For small-sized CSTNs, the original version shows the best performance, but the performance difference between the two versions decreases as the number of time-points in the CSTN increases. We conclude that the simplified algorithm is a practical alternative for checking the dynamic consistency of CSTNs