Abstract: Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve. In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to larger DEC-MDPs. Our heuristic solution method, called Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP. First, it speeds up OC-DEC-MDP by an order of magnitude by maintaining and manipulating a value function for each state (as a function of time) rather than a separate value for each pair of sate and time interval. Furthermore, it achieves better solution qualities than OC-DEC-MDP because, as our analytical results show, it does not overestimate the expected total reward like OC-DEC-MDP. We test both improvements independently in a crisis-management domain as well as for other types of domains. Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution qualities in a variety of situations.

On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints Janusz Marecki and Milind Tambe Computer Science Department University of Southern California 941 W 37th Place, Los Angeles, CA 90089 {marecki, tambe}@usc.
edu ABSTRACT Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to larger DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP.
First, it speeds up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each state (as a function of time) rather than a separate value for each pair of sate and time interval.
Furthermore, it achieves better solution qualities than OC-DEC-MDP because, as our analytical results show, it does not overestimate the expected total reward like OC-DEC- MDP.
We test both improvements independently in a crisis-management domain as well as for other types of domains.
Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution qualities in a variety of situations.
Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial IntelligenceMulti-agent Systems General Terms Algorithms, Theory 1.
INTRODUCTION The development of algorithms for effective coordination of multiple agents acting as a team in uncertain and time critical domains has recently become a very active research field with potential applications ranging from coordination of agents during a hostage rescue mission [11] to the coordination of Autonomous Mars Exploration Rovers [2].
Because of the uncertain and dynamic characteristics of such domains, decision-theoretic models have received a lot of attention in recent years, mainly thanks to their expressiveness and the ability to reason about the utility of actions over time.
Key decision-theoretic models that have become popular in the literature include Decentralized Markov Decision Processes (DECMDPs) and Decentralized, Partially Observable Markov Decision Processes (DEC-POMDPs).
Unfortunately, solving these models optimally has been proven to be NEXP-complete [3], hence more tractable subclasses of these models have been the subject of intensive research.
In particular, Network Distributed POMDP [13] which assume that not all the agents interact with each other, Transition Independent DEC-MDP [2] which assume that transition function is decomposable into local transition functions or DEC-MDP with Event Driven Interactions [1] which assume that interactions between agents happen at fixed time points constitute good examples of such subclasses.
Although globally optimal algorithms for these subclasses have demonstrated promising results, domains on which these algorithms run are still small and time horizons are limited to only a few time ticks.
To remedy that, locally optimal algorithms have been proposed [12] [4] [5].
In particular, Opportunity Cost DEC-MDP [4] [5], referred to as OC-DEC-MDP, is particularly notable, as it has been shown to scale up to domains with hundreds of tasks and double digit time horizons.
Additionally, OC-DEC-MDP is unique in its ability to address both temporal constraints and uncertain method execution durations, which is an important factor for real-world domains.
OC-DEC-MDP is able to scale up to such domains mainly because instead of searching for the globally optimal solution, it carries out a series of policy iterations; in each iteration it performs a value iteration that reuses the data computed during the previous policy iteration.
However, OC-DEC-MDP is still slow, especially as the time horizon and the number of methods approach large values.
The reason for high runtimes of OC-DEC-MDP for such domains is a consequence of its huge state space, i.e., OC-DEC-MDP introduces a separate state for each possible pair of method and method execution interval.
Furthermore, OC-DEC-MDP overestimates the reward that a method expects to receive for enabling the execution of future methods.
This reward, also referred to as the opportunity cost, plays a crucial role in agent decision making, and as we show later, its overestimation leads to highly suboptimal policies.
In this context, we present VFP (= Value Function P ropagation), an efficient solution technique for the DEC-MDP model with temporal constraints and uncertain method execution durations, that builds on the success of OC-DEC-MDP.
VFP introduces our two orthogonal ideas: First, similarly to [7] [9] and [10], we maintain 830 978-81-904262-7-5 (RPS) c 2007 IFAAMAS and manipulate a value function over time for each method rather than a separate value for each pair of method and time interval.
Such representation allows us to group the time points for which the value function changes at the same rate (= its slope is constant), which results in fast, functional propagation of value functions.
Second, we prove (both theoretically and empirically) that OC-DEC- MDP overestimates the opportunity cost, and to remedy that, we introduce a set of heuristics, that correct the opportunity cost overestimation problem.
This paper is organized as follows: In section 2 we motivate this research by introducing a civilian rescue domain where a team of fire- brigades must coordinate in order to rescue civilians trapped in a burning building.
In section 3 we provide a detailed description of our DEC-MDP model with Temporal Constraints and in section 4 we discuss how one could solve the problems encoded in our model using globally optimal and locally optimal solvers.
Sections 5 and 6 discuss the two orthogonal improvements to the state-of-the-art OC-DEC-MDP algorithm that our VFP algorithm implements.
Finally, in section 7 we demonstrate empirically the impact of our two orthogonal improvements, i.e., we show that: (i) The new heuristics correct the opportunity cost overestimation problem leading to higher quality policies, and (ii) By allowing for a systematic tradeoff of solution quality for time, the VFP algorithm runs much faster than the OC-DEC-MDP algorithm 2.
MOTIVATING EXAMPLE We are interested in domains where multiple agents must coordinate their plans over time, despite uncertainty in plan execution duration and outcome.
One example domain is large-scale disaster, like a fire in a skyscraper.
Because there can be hundreds of civilians scattered across numerous floors, multiple rescue teams have to be dispatched, and radio communication channels can quickly get saturated and useless.
In particular, small teams of fire-brigades must be sent on separate missions to rescue the civilians trapped in dozens of different locations.
Picture a small mission plan from Figure (1), where three firebrigades have been assigned a task to rescue the civilians trapped at site B, accessed from site A (e.g. an office accessed from the floor)1 .
General fire fighting procedures involve both: (i) putting out the flames, and (ii) ventilating the site to let the toxic, high temperature gases escape, with the restriction that ventilation should not be performed too fast in order to prevent the fire from spreading.
The team estimates that the civilians have 20 minutes before the fire at site B becomes unbearable, and that the fire at site A has to be put out in order to open the access to site B.
As has happened in the past in large scale disasters, communication often breaks down; and hence we assume in this domain that there is no communication between the fire-brigades 1,2 and 3 (denoted as FB1, FB2 and FB3).
Consequently, FB2 does not know if it is already safe to ventilate site A, FB1 does not know if it is already safe to enter site A and start fighting fire at site B, etc..
We assign the reward 50 for evacuating the civilians from site B, and a smaller reward 20 for the successful ventilation of site A, since the civilians themselves might succeed in breaking out from site B.
One can clearly see the dilemma, that FB2 faces: It can only estimate the durations of the Fight fire at site A methods to be executed by FB1 and FB3, and at the same time FB2 knows that time is running out for civilians.
If FB2 ventilates site A too early, the fire will spread out of control, whereas if FB2 waits with the ventilation method for too long, fire at site B will become unbearable for the civilians.
In general, agents have to perform a sequence of such 1 We explain the EST and LET notation in section 3 Figure 1: Civilian rescue domain and a mission plan.
Dotted arrows represent implicit precedence constraints within an agent.
difficult decisions; in particular, decision process of FB2 involves first choosing when to start ventilating site A, and then (depending on the time it took to ventilate site A), choosing when to start evacuating the civilians from site B.
Such sequence of decisions constitutes the policy of an agent, and it must be found fast because time is running out.
3.
MODEL DESCRIPTION We encode our decision problems in a model which we refer to as Decentralized MDP with Temporal Constraints 2 .
Each instance of our decision problems can be described as a tuple M, A, C, P, R where M = {mi} |M| i=1 is the set of methods, and A = {Ak} |A| k=1 is the set of agents.
Agents cannot communicate during mission execution.
Each agent Ak is assigned to a set Mk of methods, such that S|A| k=1 Mk = M and ∀i,j;i=jMi ∩ Mj = ø.
Also, each method of agent Ak can be executed only once, and agent Ak can execute only one method at a time.
Method execution times are uncertain and P = {pi} |M| i=1 is the set of distributions of method execution durations.
In particular, pi(t) is the probability that the execution of method mi consumes time t. C is a set of temporal constraints in the system.
Methods are partially ordered and each method has fixed time windows inside which it can be executed, i.e., C = C≺ ∪ C[ ] where C≺ is the set of predecessor constraints and C[ ] is the set of time window constraints.
For c ∈ C≺, c = mi, mj means that method mi precedes method mj i.e., execution of mj cannot start before mi terminates.
In particular, for an agent Ak, all its methods form a chain linked by predecessor constraints.
We assume, that the graph G = M, C≺ is acyclic, does not have disconnected nodes (the problem cannot be decomposed into independent subproblems), and its source and sink vertices identify the source and sink methods of the system.
For c ∈ C[ ], c = mi, EST, LET means that execution of mi can only start after the Earliest Starting Time EST and must finish before the Latest End Time LET; we allow methods to have multiple disjoint time window constraints.
Although distributions pi can extend to infinite time horizons, given the time window constraints, the planning horizon Δ = max m,τ,τ ∈C[ ] τ is considered as the mission deadline.
Finally, R = {ri} |M| i=1 is the set of non-negative rewards, i.e., ri is obtained upon successful execution of mi.
Since there is no communication allowed, an agent can only estimate the probabilities that its methods have already been enabled 2 One could also use the OC-DEC-MDP framework, which models both time and resource constraints The Sixth Intl..
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on Autonomous Agents and Multi-Agent Systems (AAMAS 07) 831 by other agents.
Consequently, if mj ∈ Mk is the next method to be executed by the agent Ak and the current time is t ∈ [0, Δ], the agent has to make a decision whether to Execute the method mj (denoted as E), or to Wait (denoted as W).
In case agent Ak decides to wait, it remains idle for an arbitrary small time , and resumes operation at the same place (= about to execute method mj) at time t + .
In case agent Ak decides to Execute the next method, two outcomes are possible: Success: The agent Ak receives reward rj and moves on to its next method (if such method exists) so long as the following conditions hold: (i) All the methods {mi| mi, mj ∈ C≺} that directly enable method mj have already been completed, (ii) Execution of method mj started in some time window of method mj, i.e., ∃ mj ,τ,τ ∈C[ ] such that t ∈ [τ, τ ], and (iii) Execution of method mj finished inside the same time window, i.e., agent Ak completed method mj in time less than or equal to τ − t. Failure: If any of the above-mentioned conditions does not hold, agent Ak stops its execution.
Other agents may continue their execution, but methods mk ∈ {m| mj, m ∈ C≺} will never become enabled.
The policy πk of an agent Ak is a function πk : Mk × [0, Δ] → {W, E}, and πk( m, t ) = a means, that if Ak is at method m at time t, it will choose to perform the action a.
A joint policy π = [πk] |A| k=1 is considered to be optimal (denoted as π∗ ), if it maximizes the sum of expected rewards for all the agents.
4.
SOLUTION TECHNIQUES 4.1 Optimal Algorithms Optimal joint policy π∗ is usually found by using the Bellman update principle, i.e., in order to determine the optimal policy for method mj, optimal policies for methods mk ∈ {m| mj, m ∈ C≺} are used.
Unfortunately, for our model, the optimal policy for method mj also depends on policies for methods mi ∈ {m| m, mj ∈ C≺}.
This double dependency results from the fact, that the expected reward for starting the execution of method mj at time t also depends on the probability that method mj will be enabled by time t. Consequently, if time is discretized, one needs to consider Δ|M| candidate policies in order to find π∗ .
Thus, globally optimal algorithms used for solving real-world problems are unlikely to terminate in reasonable time [11].
The complexity of our model could be reduced if we considered its more restricted version; in particular, if each method mj was allowed to be enabled at time points t ∈ Tj ⊂ [0, Δ], the Coverage Set Algorithm (CSA) [1] could be used.
However, CSA complexity is double exponential in the size of Ti, and for our domains Tj can store all values ranging from 0 to Δ.
4.2 Locally Optimal Algorithms Following the limited applicability of globally optimal algorithms for DEC-MDPs with Temporal Constraints, locally optimal algorithms appear more promising.
Specially, the OC-DEC-MDP algorithm [4] is particularly significant, as it has shown to easily scale up to domains with hundreds of methods.
The idea of the OC-DECMDP algorithm is to start with the earliest starting time policy π0 (according to which an agent will start executing the method m as soon as m has a non-zero chance of being already enabled), and then improve it iteratively, until no further improvement is possible.
At each iteration, the algorithm starts with some policy π, which uniquely determines the probabilities Pi,[τ,τ ] that method mi will be performed in the time interval [τ, τ ].
It then performs two steps: Step 1: It propagates from sink methods to source methods the values Vi,[τ,τ ], that represent the expected utility for executing method mi in the time interval [τ, τ ].
This propagation uses the probabilities Pi,[τ,τ ] from previous algorithm iteration.
We call this step a value propagation phase.
Step 2: Given the values Vi,[τ,τ ] from Step 1, the algorithm chooses the most profitable method execution intervals which are stored in a new policy π .
It then propagates the new probabilities Pi,[τ,τ ] from source methods to sink methods.
We call this step a probability propagation phase.
If policy π does not improve π, the algorithm terminates.
There are two shortcomings of the OC-DEC-MDP algorithm that we address in this paper.
First, each of OC-DEC-MDP states is a pair mj, [τ, τ ] , where [τ, τ ] is a time interval in which method mj can be executed.
While such state representation is beneficial, in that the problem can be solved with a standard value iteration algorithm, it blurs the intuitive mapping from time t to the expected total reward for starting the execution of mj at time t. Consequently, if some method mi enables method mj, and the values Vj,[τ,τ ]∀τ,τ ∈[0,Δ] are known, the operation that calculates the values Vi,[τ,τ ]∀τ, τ ∈ [0, Δ] (during the value propagation phase), runs in time O(I2 ), where I is the number of time intervals 3 .
Since the runtime of the whole algorithm is proportional to the runtime of this operation, especially for big time horizons Δ, the OC- DECMDP algorithm runs slow.
Second, while OC-DEC-MDP emphasizes on precise calculation of values Vj,[τ,τ ], it fails to address a critical issue that determines how the values Vj,[τ,τ ] are split given that the method mj has multiple enabling methods.
As we show later, OC-DEC-MDP splits Vj,[τ,τ ] into parts that may overestimate Vj,[τ,τ ] when summed up again.
As a result, methods that precede the method mj overestimate the value for enabling mj which, as we show later, can have disastrous consequences.
In the next two sections, we address both of these shortcomings.
5.
VALUE FUNCTION PROPAGATION (VFP) The general scheme of the VFP algorithm is identical to the OCDEC-MDP algorithm, in that it performs a series of policy improvement iterations, each one involving a Value and Probability Propagation Phase.
However, instead of propagating separate values, VFP maintains and propagates the whole functions, we therefore refer to these phases as the value function propagation phase and the probability function propagation phase.
To this end, for each method mi ∈ M, we define three new functions: Value Function, denoted as vi(t), that maps time t ∈ [0, Δ] to the expected total reward for starting the execution of method mi at time t. Opportunity Cost Function, denoted as Vi(t), that maps time t ∈ [0, Δ] to the expected total reward for starting the execution of method mi at time t assuming that mi is enabled.
Probability Function, denoted as Pi(t), that maps time t ∈ [0, Δ] to the probability that method mi will be completed before time t.
Such functional representation allows us to easily read the current policy, i.e., if an agent Ak is at method mi at time t, then it will wait as long as value function vi(t) will be greater in the future.
Formally: πk( mi, t ) = j W if ∃t >t such that vi(t) < vi(t ) E otherwise.
We now develop an analytical technique for performing the value function and probability function propagation phases.
3 Similarly for the probability propagation phase 832 The Sixth Intl..
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on Autonomous Agents and Multi-Agent Systems (AAMAS 07) 5.1 Value Function Propagation Phase Suppose, that we are performing a value function propagation phase during which the value functions are propagated from the sink methods to the source methods.
At any time during this phase we encounter a situation shown in Figure 2, where opportunity cost functions [Vjn ]N n=0 of methods [mjn ]N n=0 are known, and the opportunity cost Vi0 of method mi0 is to be derived.
Let pi0 be the probability distribution function of method mi0 execution duration, and ri0 be the immediate reward for starting and completing the execution of method mi0 inside a time interval [τ, τ ] such that mi0 τ, τ ∈ C[ ].
The function Vi0 is then derived from ri0 and opportunity costs Vjn,i0 (t) n = 1, ..., N from future methods.
Formally: Vi0 (t) = 8 >>< >>: R τ −t 0 pi0 (t )(ri0 + PN n=0 Vjn,i0 (t + t ))dt if ∃ mi0 τ,τ ∈C[ ] such that t ∈ [τ, τ ] 0 otherwise (1) Note, that for t ∈ [τ, τ ], if h(t) := ri0 + PN n=0 Vjn,i0 (τ −t) then Vi0 is a convolution of p and h: vi0 (t) = (pi0 ∗h)(τ −t).
Assume for now, that Vjn,i0 represents a full opportunity cost, postponing the discussion on different techniques for splitting the opportunity cost Vj0 into [Vj0,ik ]K k=0 until section 6.
We now show how to derive Vj0,i0 (derivation of Vjn,i0 for n = 0 follows the same scheme).
Figure 2: Fragment of an MDP of agent Ak.
Probability functions propagate forward (left to right) whereas value functions propagate backward (right to left).
Let V j0,i0 (t) be the opportunity cost of starting the execution of method mj0 at time t given that method mi0 has been completed.
It is derived by multiplying Vi0 by the probability functions of all methods other than mi0 that enable mj0 .
Formally: V j0,i0 (t) = Vj0 (t) · KY k=1 Pik (t).
Where similarly to [4] and [5] we ignored the dependency of [Plk ]K k=1.
Observe that V j0,i0 does not have to be monotonically decreasing, i.e., delaying the execution of the method mi0 can sometimes be profitable.
Therefore the opportunity cost Vj0,i0 (t) of enabling method mi0 at time t must be greater than or equal to V j0,i0 .
Furthermore, Vj0,i0 should be non-increasing.
Formally: Vj0,i0 = min f∈F f (2) Where F = {f | f ≥ V j0,i0 and f(t) ≥ f(t ) ∀t<t }.
Knowing the opportunity cost Vi0 , we can then easily derive the value function vi0 .
Let Ak be an agent assigned to the method mi0 .
If Ak is about to start the execution of mi0 it means, that Ak must have completed its part of the mission plan up to the method mi0 .
Since Ak does not know if other agents have completed methods [mlk ]k=K k=1 , in order to derive vi0 , it has to multiply Vi0 by the probability functions of all methods of other agents that enable mi0 .
Formally: vi0 (t) = Vi0 (t) · KY k=1 Plk (t) Where the dependency of [Plk ]K k=1 is also ignored.
We have consequently shown a general scheme how to propagate the value functions: Knowing [vjn ]N n=0 and [Vjn ]N n=0 of methods [mjn ]N n=0 we can derive vi0 and Vi0 of method mi0 .
In general, the value function propagation scheme starts with sink nodes.
It then visits at each time a method m, such that all the methods that m enables have already been marked as visited.
The value function propagation phase terminates when all the source methods have been marked as visited.
5.2 Reading the Policy In order to determine the policy of agent Ak for the method mj0 we must identify the set Zj0 of intervals [z, z ] ⊂ [0, ..., Δ], such that: ∀t∈[z,z ] πk( mj0 , t ) = W.
One can easily identify the intervals of Zj0 by looking at the time intervals in which the value function vj0 does not decrease monotonically.
5.3 Probability Function Propagation Phase Assume now, that value functions and opportunity cost values have all been propagated from sink methods to source nodes and the sets Zj for all methods mj ∈ M have been identified.
Since value function propagation phase was using probabilities Pi(t) for methods mi ∈ M and times t ∈ [0, Δ] found at previous algorithm iteration, we now have to find new values Pi(t), in order to prepare the algorithm for its next iteration.
We now show how in the general case (Figure 2) propagate the probability functions forward through one method, i.e., we assume that the probability functions [Pik ]K k=0 of methods [mik ]K k=0 are known, and the probability function Pj0 of method mj0 must be derived.
Let pj0 be the probability distribution function of method mj0 execution duration, and Zj0 be the set of intervals of inactivity for method mj0 , found during the last value function propagation phase.
If we ignore the dependency of [Pik ]K k=0 then the probability Pj0 (t) that the execution of method mj0 starts before time t is given by: Pj0 (t) = (QK k=0 Pik (τ) if ∃(τ, τ ) ∈ Zj0 s.t. t ∈ (τ, τ ) QK k=0 Pik (t) otherwise.
Given Pj0 (t), the probability Pj0 (t) that method mj0 will be completed by time t is derived by: Pj0 (t) = Z t 0 Z t 0 ( ∂Pj0 ∂t )(t ) · pj0 (t − t )dt dt (3) Which can be written compactly as ∂Pj0 ∂t = pj0 ∗ ∂P j0 ∂t .
We have consequently shown how to propagate the probability functions [Pik ]K k=0 of methods [mik ]K k=0 to obtain the probability function Pj0 of method mj0 .
The general, the probability function propagation phase starts with source methods msi for which we know that Psi = 1 since they are enabled by default.
We then visit at each time a method m such that all the methods that enable The Sixth Intl..
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on Autonomous Agents and Multi-Agent Systems (AAMAS 07) 833 m have already been marked as visited.
The probability function propagation phase terminates when all the sink methods have been marked as visited.
5.4 The Algorithm Similarly to the OC-DEC-MDP algorithm, VFP starts the policy improvement iterations with the earliest starting time policy π0 .
Then at each iteration it: (i) Propagates the value functions [vi] |M| i=1 using the old probability functions [Pi] |M| i=1 from previous algorithm iteration and establishes the new sets [Zi] |M| i=1 of method inactivity intervals, and (ii) propagates the new probability functions [Pi ] |M| i=1 using the newly established sets [Zi] |M| i=1.
These new functions [Pi ] |M| i=1 are then used in the next iteration of the algorithm.
Similarly to OC-DEC-MDP, VFP terminates if a new policy does not improve the policy from the previous algorithm iteration.
5.5 Implementation of Function Operations So far, we have derived the functional operations for value function and probability function propagation without choosing any function representation.
In general, our functional operations can handle continuous time, and one has freedom to choose a desired function approximation technique, such as piecewise linear [7] or piecewise constant [9] approximation.
However, since one of our goals is to compare VFP with the existing OC-DEC- MDP algorithm, that works only for discrete time, we also discretize time, and choose to approximate value functions and probability functions with piecewise linear (PWL) functions.
When the VFP algorithm propagates the value functions and probability functions, it constantly carries out operations represented by equations (1) and (3) and we have already shown that these operations are convolutions of some functions p(t) and h(t).
If time is discretized, functions p(t) and h(t) are discrete; however, h(t) can be nicely approximated with a PWL function bh(t), which is exactly what VFP does.
As a result, instead of performing O(Δ2 ) multiplications to compute f(t), VFP only needs to perform O(k · Δ) multiplications to compute f(t), where k is the number of linear segments of bh(t) (note, that since h(t) is monotonic, bh(t) is usually close to h(t) with k Δ).
Since Pi values are in range [0, 1] and Vi values are in range [0, P mi∈M ri], we suggest to approximate Vi(t) with bVi(t) within error V , and Pi(t) with bPi(t) within error P .
We now prove that the overall approximation error accumulated during the value function propagation phase can be expressed in terms of P and V : THEOREM 1.
Let C≺ be a set of precedence constraints of a DEC-MDP with Temporal Constraints, and P and V be the probability function and value function approximation errors respectively.
The overall error π = maxV supt∈[0,Δ]|V (t) − bV (t)| of value function propagation phase is then bounded by: |C≺| V + ((1 + P )|C≺| − 1) P mi∈M ri .
PROOF.
In order to establish the bound for π, we first prove by induction on the size of C≺, that the overall error of probability function propagation phase, π(P ) = maxP supt∈[0,Δ]|P(t) − bP(t)| is bounded by (1 + P )|C≺| − 1.
Induction base: If n = 1 only two methods are present, and we will perform the operation identified by Equation (3) only once, introducing the error π(P ) = P = (1 + P )|C≺| − 1.
Induction step: Suppose, that π(P ) for |C≺| = n is bounded by (1 + P )n − 1, and we want to prove that this statement holds for |C≺| = n. Let G = M, C≺ be a graph with at most n + 1 edges, and G = M, C≺ be a subgraph of G, such that C≺ = C≺ − { mi, mj }, where mj ∈ M is a sink node in G. From the induction assumption we have, that C≺ introduces the probability propagation phase error bounded by (1 + P )n − 1.
We now add back the link { mi, mj } to C≺, which affects the error of only one probability function, namely Pj, by a factor of (1 + P ).
Since probability propagation phase error in C≺ was bounded by (1 + P )n − 1, in C≺ = C≺ ∪ { mi, mj } it can be at most ((1 + P )n − 1)(1 + P ) < (1 + P )n+1 − 1.
Thus, if opportunity cost functions are not overestimated, they are bounded by P mi∈M ri and the error of a single value function propagation operation will be at most Z Δ 0 p(t)( V +((1+ P ) |C≺| −1) X mi∈M ri) dt < V +((1+ P ) |C≺| −1) X mi∈M ri.
Since the number of value function propagation operations is |C≺|, the total error π of the value function propagation phase is bounded by: |C≺| V + ((1 + P )|C≺| − 1) P mi∈M ri .
6.
SPLITTING THE OPPORTUNITY COST FUNCTIONS In section 5 we left out the discussion about how the opportunity cost function Vj0 of method mj0 is split into opportunity cost functions [Vj0,ik ]K k=0 sent back to methods [mik ]K k=0 , that directly enable method mj0 .
So far, we have taken the same approach as in [4] and [5] in that the opportunity cost function Vj0,ik that the method mik sends back to the method mj0 is a minimal, non-increasing function that dominates function V j0,ik (t) = (Vj0 · Q k ∈{0,...,K} k =k Pik )(t).
We refer to this approach, as heuristic H 1,1 .
Before we prove that this heuristic overestimates the opportunity cost, we discuss three problems that might occur when splitting the opportunity cost functions: (i) overestimation, (ii) underestimation and (iii) starvation.
Consider the situation in Figure Figure 3: Splitting the value function of method mj0 among methods [mik ]K k=0.
(3) when value function propagation for methods [mik ]K k=0 is performed.
For each k = 0, ..., K, Equation (1) derives the opportunity cost function Vik from immediate reward rk and opportunity cost function Vj0,ik .
If m0 is the only methods that precedes method mk, then V ik,0 = Vik is propagated to method m0, and consequently the opportunity cost for completing the method m0 at time t is equal to PK k=0 Vik,0(t).
If this cost is overestimated, then an agent A0 at method m0 will have too much incentive to finish the execution of m0 at time t. Consequently, although the probability P(t) that m0 will be enabled by other agents by time t is low, agent A0 might still find the expected utility of starting the execution of m0 at time t higher than the expected utility of doing it later.
As a result, it will choose at time t to start executing method m0 instead of waiting, which can have disastrous consequences.
Similarly, if PK k=0 Vik,0(t) is underestimated, agent A0 might loose interest in enabling the future methods [mik ]K k=0 and just focus on 834 The Sixth Intl..
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on Autonomous Agents and Multi-Agent Systems (AAMAS 07) maximizing the chance of obtaining its immediate reward r0.
Since this chance is increased when agent A0 waits4 , it will consider at time t to be more profitable to wait, instead of starting the execution of m0, which can have similarly disastrous consequences.
Finally, if Vj0 is split in a way, that for some k, Vj0,ik = 0, it is the method mik that underestimates the opportunity cost of enabling method mj0 , and the similar reasoning applies.
We call such problem a starvation of method mk.
That short discussion shows the importance of splitting the opportunity cost function Vj0 in such a way, that overestimation, underestimation, and starvation problem is avoided.
We now prove that: THEOREM 2.
Heuristic H 1,1 can overestimate the opportunity cost.
PROOF.
We prove the theorem by showing a case where the overestimation occurs.
For the mission plan from Figure (3), let H 1,1 split Vj0 into [V j0,ik = Vj0 · Q k ∈{0,...,K} k =k Pik ]K k=0 sent to methods [mik ]K k=0 respectively.
Also, assume that methods [mik ]K k=0 provide no local reward and have the same time windows, i.e., rik = 0; ESTik = 0, LETik = Δ for k = 0, ..., K. To prove the overestimation of opportunity cost, we must identify t0 ∈ [0, ..., Δ] such that the opportunity cost PK k=0 Vik (t) for methods [mik ]K k=0 at time t ∈ [0, .
.
, Δ] is greater than the opportunity cost Vj0 (t).
From Equation (1) we have: Vik (t) = Z Δ−t 0 pik (t )Vj0,ik (t + t )dt Summing over all methods [mik ]K k=0 we obtain: KX k=0 Vik (t) = KX k=0 Z Δ−t 0 pik (t )Vj0,ik (t + t )dt (4) ≥ KX k=0 Z Δ−t 0 pik (t )V j0,ik (t + t )dt = KX k=0 Z Δ−t 0 pik (t )Vj0 (t + t ) Y k ∈{0,...,K} k =k Pik (t + t )dt Let c ∈ (0, 1] be a constant and t0 ∈ [0, Δ] be such that ∀t>t0 and ∀k=0,.
.
,K we have Q k ∈{0,...,K} k =k Pik (t) > c. Then: KX k=0 Vik (t0) > KX k=0 Z Δ−t0 0 pik (t )Vj0 (t0 + t ) · c dt Because Pjk is non-decreasing.
Now, suppose there exists t1 ∈ (t0, Δ], such that PK k=0 R t1−t0 0 pik (t )dt > Vj0 (t0) c·Vj0 (t1) .
Since decreasing the upper limit of the integral over positive function also decreases the integral, we have: KX k=0 Vik (t0) > c KX k=0 Z t1 t0 pik (t − t0)Vj0 (t )dt And since Vj0 (t ) is non-increasing we have: KX k=0 Vik (t0) > c · Vj0 (t1) KX k=0 Z t1 t0 pik (t − t0)dt (5) = c · Vj0 (t1) KX k=0 Z t1−t0 0 pik (t )dt > c · Vj0 (t1) Vj(t0) c · Vj(t1) = Vj(t0) 4 Assuming LET0 t Consequently, the opportunity cost PK k=0 Vik (t0) of starting the execution of methods [mik ]K k=0 at time t ∈ [0, .
.
, Δ] is greater than the opportunity cost Vj0 (t0) which proves the theorem.Figure 4 shows that the overestimation of opportunity cost is easily observable in practice.
To remedy the problem of opportunity cost overestimation, we propose three alternative heuristics that split the opportunity cost functions: • Heuristic H 1,0 : Only one method, mik gets the full expected reward for enabling method mj0 , i.e., V j0,ik (t) = 0 for k ∈ {0, ..., K}\{k} and V j0,ik (t) = (Vj0 · Q k ∈{0,...,K} k =k Pik )(t).
• Heuristic H 1/2,1/2 : Each method [mik ]K k=0 gets the full opportunity cost for enabling method mj0 divided by the number K of methods enabling the method mj0 , i.e., V j0,ik (t) = 1 K (Vj0 · Q k ∈{0,...,K} k =k Pik )(t) for k ∈ {0, ..., K}.
• Heuristic bH 1,1 : This is a normalized version of the H 1,1 heuristic in that each method [mik ]K k=0 initially gets the full opportunity cost for enabling the method mj0 .
To avoid opportunity cost overestimation, we normalize the split functions when their sum exceeds the opportunity cost function to be split.
Formally: V j0,ik (t) = 8 >< >: V H 1,1 j0,ik (t) if PK k=0 V H 1,1 j0,ik (t) < Vj0 (t) Vj0 (t) V H 1,1 j0,ik (t) PK k=0 V H 1,1 j0,ik (t) otherwise Where V H 1,1 j0,ik (t) = (Vj0 · Q k ∈{0,...,K} k =k Pjk )(t).
For the new heuristics, we now prove, that: THEOREM 3.
Heuristics H 1,0 , H 1/2,1/2 and bH 1,1 do not overestimate the opportunity cost.
PROOF.
When heuristic H 1,0 is used to split the opportunity cost function Vj0 , only one method (e.g. mik ) gets the opportunity cost for enabling method mj0 .
Thus: KX k =0 Vik (t) = Z Δ−t 0 pik (t )Vj0,ik (t + t )dt (6) And since Vj0 is non-increasing ≤ Z Δ−t 0 pik (t )Vj0 (t + t ) · Y k ∈{0,...,K} k =k Pjk (t + t )dt ≤ Z Δ−t 0 pik (t )Vj0 (t + t )dt ≤ Vj0 (t) The last inequality is also a consequence of the fact that Vj0 is non-increasing.
For heuristic H 1/2,1/2 we similarly have: KX k=0 Vik (t) ≤ KX k=0 Z Δ−t 0 pik (t ) 1 K Vj0 (t + t ) Y k ∈{0,...,K} k =k Pjk (t + t )dt ≤ 1 K KX k=0 Z Δ−t 0 pik (t )Vj0 (t + t )dt ≤ 1 K · K · Vj0 (t) = Vj0 (t).
For heuristic bH 1,1 , the opportunity cost function Vj0 is by definition split in such manner, that PK k=0 Vik (t) ≤ Vj0 (t).
Consequently, we have proved, that our new heuristics H 1,0 , H 1/2,1/2 and bH 1,1 avoid the overestimation of the opportunity cost.
The Sixth Intl..
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on Autonomous Agents and Multi-Agent Systems (AAMAS 07) 835 The reason why we have introduced all three new heuristics is the following: Since H 1,1 overestimates the opportunity cost, one has to choose which method mik will receive the reward from enabling the method mj0 , which is exactly what the heuristic H 1,0 does.
However, heuristic H 1,0 leaves K − 1 methods that precede the method mj0 without any reward which leads to starvation.
Starvation can be avoided if opportunity cost functions are split using heuristic H 1/2,1/2 , that provides reward to all enabling methods.
However, the sum of split opportunity cost functions for the H 1/2,1/2 heuristic can be smaller than the non-zero split opportunity cost function for the H 1,0 heuristic, which is clearly undesirable.
Such situation (Figure 4, heuristic H 1,0 ) occurs because the mean f+g 2 of two functions f, g is not smaller than f nor g only if f = g.
This is why we have proposed the bH 1,1 heuristic, which by definition avoids the overestimation, underestimation and starvation problems.
7.
EXPERIMENTAL EVALUATION Since the VFP algorithm that we introduced provides two orthogonal improvements over the OC-DEC-MDP algorithm, the experimental evaluation we performed consisted of two parts: In part 1, we tested empirically the quality of solutions that an locally optimal solver (either OC-DEC-MDP or VFP) finds, given it uses different opportunity cost function splitting heuristic, and in part 2, we compared the runtimes of the VFP and OC-DEC- MDP algorithms for a variety of mission plan configurations.
Part 1: We first ran the VFP algorithm on a generic mission plan configuration from Figure 3 where only methods mj0 , mi1 , mi2 and m0 were present.
Time windows of all methods were set to 400, duration pj0 of method mj0 was uniform, i.e., pj0 (t) = 1 400 and durations pi1 , pi2 of methods mi1 , mi2 were normal distributions, i.e., pi1 = N(μ = 250, σ = 20), and pi2 = N(μ = 200, σ = 100).
We assumed that only method mj0 provided reward, i.e. rj0 = 10 was the reward for finishing the execution of method mj0 before time t = 400.
We show our results in Figure (4) where the x-axis of each of the graphs represents time whereas the y-axis represents the opportunity cost.
The first graph confirms, that when the opportunity cost function Vj0 was split into opportunity cost functions Vi1 and Vi2 using the H 1,1 heuristic, the function Vi1 +Vi2 was not always below the Vj0 function.
In particular, Vi1 (280) + Vi2 (280) exceeded Vj0 (280) by 69%.
When heuristics H 1,0 , H 1/2,1/2 and bH 1,1 were used (graphs 2,3 and 4), the function Vi1 + Vi2 was always below Vj0 .
We then shifted our attention to the civilian rescue domain introduced in Figure 1 for which we sampled all action execution durations from the normal distribution N = (μ = 5, σ = 2)).
To obtain the baseline for the heuristic performance, we implemented a globally optimal solver, that found a true expected total reward for this domain (Figure (6a)).
We then compared this reward with a expected total reward found by a locally optimal solver guided by each of the discussed heuristics.
Figure (6a), which plots on the y-axis the expected total reward of a policy complements our previous results: H 1,1 heuristic overestimated the expected total reward by 280% whereas the other heuristics were able to guide the locally optimal solver close to a true expected total reward.
Part 2: We then chose H 1,1 to split the opportunity cost functions and conducted a series of experiments aimed at testing the scalability of VFP for various mission plan configurations, using the performance of the OC-DEC-MDP algorithm as a benchmark.
We began the VFP scalability tests with a configuration from Figure (5a) associated with the civilian rescue domain, for which method execution durations were extended to normal distributions N(μ = Figure 5: Mission plan configurations: (a) civilian rescue domain, (b) chain of n methods, (c) tree of n methods with branching factor = 3 and (d) square mesh of n methods.
Figure 6: VFP performance in the civilian rescue domain.
30, σ = 5), and the deadline was extended to Δ = 200.
We decided to test the runtime of the VFP algorithm running with three different levels of accuracy, i.e., different approximation parameters P and V were chosen, such that the cumulative error of the solution found by VFP stayed within 1%, 5% and 10% of the solution found by the OC- DEC-MDP algorithm.
We then run both algorithms for a total of 100 policy improvement iterations.
Figure (6b) shows the performance of the VFP algorithm in the civilian rescue domain (y-axis shows the runtime in milliseconds).
As we see, for this small domain, VFP runs 15% faster than OCDEC-MDP when computing the policy with an error of less than 1%.
For comparison, the globally optimal solved did not terminate within the first three hours of its runtime which shows the strength of the opportunistic solvers, like OC-DEC-MDP.
We next decided to test how VFP performs in a more difficult domain, i.e., with methods forming a long chain (Figure (5b)).
We tested chains of 10, 20 and 30 methods, increasing at the same time method time windows to 350, 700 and 1050 to ensure that later methods can be reached.
We show the results in Figure (7a), where we vary on the x-axis the number of methods and plot on the y-axis the algorithm runtime (notice the logarithmic scale).
As we observe, scaling up the domain reveals the high performance of VFP: Within 1% error, it runs up to 6 times faster than OC-DECMDP.
We then tested how VFP scales up, given that the methods are arranged into a tree (Figure (5c)).
In particular, we considered trees with branching factor of 3, and depth of 2, 3 and 4, increasing at the same time the time horizon from 200 to 300, and then to 400.
We show the results in Figure (7b).
Although the speedups are smaller than in case of a chain, the VFP algorithm still runs up to 4 times faster than OC-DEC-MDP when computing the policy with an error of less than 1%.
We finally tested how VFP handles the domains with methods arranged into a n × n mesh, i.e., C≺ = { mi,j, mk,j+1 } for i = 1, ..., n; k = 1, ..., n; j = 1, ..., n − 1.
In particular, we consider 836 The Sixth Intl..
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Figure 7: Scalability experiments for OC-DEC-MDP and VFP for different network configurations.
meshes of 3×3, 4×4, and 5×5 methods.
For such configurations we have to greatly increase the time horizon since the probabilities of enabling the final methods by a particular time decrease exponentially.
We therefore vary the time horizons from 3000 to 4000, and then to 5000.
We show the results in Figure (7c) where, especially for larger meshes, the VFP algorithm runs up to one order of magnitude faster than OC-DEC-MDP while finding a policy that is within less than 1% from the policy found by OC- DECMDP.
8.
CONCLUSIONS Decentralized Markov Decision Process (DEC-MDP) has been very popular for modeling of agent-coordination problems, it is very difficult to solve, especially for the real-world domains.
In this paper, we improved a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to large DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), provided two orthogonal improvements of OC-DEC-MDP: (i) It speeded up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each method rather than a separate value for each pair of method and time interval, and (ii) it achieved better solution qualities than OC-DEC-MDP because it corrected the overestimation of the opportunity cost of OC-DEC-MDP.
In terms of related work, we have extensively discussed the OCDEC-MDP algorithm [4].
Furthermore, as discussed in Section 4, there are globally optimal algorithms for solving DEC-MDPs with temporal constraints [1] [11].
Unfortunately, they fail to scale up to large-scale domains at present time.
Beyond OC-DEC-MDP, there are other locally optimal algorithms for DEC-MDPs and DECPOMDPs [8] [12], [13], yet, they have traditionally not dealt with uncertain execution times and temporal constraints.
Finally, value function techniques have been studied in context of single agent MDPs [7] [9].
However, similarly to [6], they fail to address the lack of global state knowledge, which is a fundamental issue in decentralized planning.
Acknowledgments This material is based upon work supported by the DARPA/IPTO COORDINATORS program and the Air Force Research Laboratory under Contract No.
FA875005C0030.
The authors also want to thank Sven Koenig and anonymous reviewers for their valuable comments.
9.
REFERENCES [1] R. Becker, V. Lesser, and S. Zilberstein.
Decentralized MDPs with Event-Driven Interactions.
In AAMAS, pages 302-309, 2004.
[2] R. Becker, S. Zilberstein, V. Lesser, and C. V. Goldman.
Transition-Independent Decentralized Markov Decision Processes.
In AAMAS, pages 41-48, 2003.
[3] D. S. Bernstein, S. Zilberstein, and N. Immerman.
The complexity of decentralized control of Markov decision processes.
In UAI, pages 32-37, 2000.
[4] A. Beynier and A. Mouaddib.
A polynomial algorithm for decentralized Markov decision processes with temporal constraints.
In AAMAS, pages 963-969, 2005.
[5] A. Beynier and A. Mouaddib.
An iterative algorithm for solving constrained decentralized Markov decision processes.
In AAAI, pages 1089-1094, 2006.
[6] C. Boutilier.
Sequential optimality and coordination in multiagent systems.
In IJCAI, pages 478-485, 1999.
[7] J. Boyan and M. Littman.
Exact solutions to time-dependent MDPs.
In NIPS, pages 1026-1032, 2000.
[8] C. Goldman and S. Zilberstein.
Optimizing information exchange in cooperative multi-agent systems, 2003.
[9] L. Li and M. Littman.
Lazy approximation for solving continuous finite-horizon MDPs.
In AAAI, pages 1175-1180, 2005.
[10] Y. Liu and S. Koenig.
Risk-sensitive planning with one-switch utility functions: Value iteration.
In AAAI, pages 993-999, 2005.
[11] D. Musliner, E. Durfee, J. Wu, D. Dolgov, R. Goldman, and M. Boddy.
Coordinated plan management using multiagent MDPs.
In AAAI Spring Symposium, 2006.
[12] R. Nair, M. Tambe, M. Yokoo, D. Pynadath, and S. Marsella.
Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings.
In IJCAI, pages 705-711, 2003.
[13] R. Nair, P. Varakantham, M. Tambe, and M. Yokoo.
Networked distributed POMDPs: A synergy of distributed constraint optimization and POMDPs.
In IJCAI, pages 1758-1760, 2005.
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On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to larger DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP.
First, it speeds up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each state (as a function of time) rather than a separate value for each pair of sate and time interval.
Furthermore, it achieves better solution qualities than OC-DEC-MDP because, as our analytical results show, it does not overestimate the expected total reward like OC-DEC - MDP.
We test both improvements independently in a crisis-management domain as well as for other types of domains.
Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution qualities in a variety of situations.
1.
INTRODUCTION
The development of algorithms for effective coordination of multiple agents acting as a team in uncertain and time critical domains has recently become a very active research field with potential applications ranging from coordination of agents during a hostage rescue mission [11] to the coordination of Autonomous Mars Explo
ration Rovers [2].
Because of the uncertain and dynamic characteristics of such domains, decision-theoretic models have received a lot of attention in recent years, mainly thanks to their expressiveness and the ability to reason about the utility of actions over time.
Key decision-theoretic models that have become popular in the literature include Decentralized Markov Decision Processes (DECMDPs) and Decentralized, Partially Observable Markov Decision Processes (DEC-POMDPs).
Unfortunately, solving these models optimally has been proven to be NEXP-complete [3], hence more tractable subclasses of these models have been the subject of intensive research.
In particular, Network Distributed POMDP [13] which assume that not all the agents interact with each other, Transition Independent DEC-MDP [2] which assume that transition function is decomposable into local transition functions or DEC-MDP with Event Driven Interactions [1] which assume that interactions between agents happen at fixed time points constitute good examples of such subclasses.
Although globally optimal algorithms for these subclasses have demonstrated promising results, domains on which these algorithms run are still small and time horizons are limited to only a few time ticks.
To remedy that, locally optimal algorithms have been proposed [12] [4] [5].
In particular, Opportunity Cost DEC-MDP [4] [5], referred to as OC-DEC-MDP, is particularly notable, as it has been shown to scale up to domains with hundreds of tasks and double digit time horizons.
Additionally, OC-DEC-MDP is unique in its ability to address both temporal constraints and uncertain method execution durations, which is an important factor for real-world domains.
OC-DEC-MDP is able to scale up to such domains mainly because instead of searching for the globally optimal solution, it carries out a series of policy iterations; in each iteration it performs a value iteration that reuses the data computed during the previous policy iteration.
However, OC-DEC-MDP is still slow, especially as the time horizon and the number of methods approach large values.
The reason for high runtimes of OC-DEC-MDP for such domains is a consequence of its huge state space, i.e., OC-DEC-MDP introduces a separate state for each possible pair of method and method execution interval.
Furthermore, OC-DEC-MDP overestimates the reward that a method expects to receive for enabling the execution of future methods.
This reward, also referred to as the opportunity cost, plays a crucial role in agent decision making, and as we show later, its overestimation leads to highly suboptimal policies.
In this context, we present VFP (= Value Function P ropagation), an efficient solution technique for the DEC-MDP model with temporal constraints and uncertain method execution durations, that builds on the success of OC-DEC-MDP.
VFP introduces our two orthogonal ideas: First, similarly to [7] [9] and [10], we maintain
and manipulate a value function over time for each method rather than a separate value for each pair of method and time interval.
Such representation allows us to group the time points for which the value function changes at the same rate (= its slope is constant), which results in fast, functional propagation of value functions.
Second, we prove (both theoretically and empirically) that OC-DEC - MDP overestimates the opportunity cost, and to remedy that, we introduce a set of heuristics, that correct the opportunity cost overestimation problem.
This paper is organized as follows: In section 2 we motivate this research by introducing a civilian rescue domain where a team of fire - brigades must coordinate in order to rescue civilians trapped in a burning building.
In section 3 we provide a detailed description of our DEC-MDP model with Temporal Constraints and in section 4 we discuss how one could solve the problems encoded in our model using globally optimal and locally optimal solvers.
Sections 5 and 6 discuss the two orthogonal improvements to the state-of-the-art OC-DEC-MDP algorithm that our VFP algorithm implements.
Finally, in section 7 we demonstrate empirically the impact of our two orthogonal improvements, i.e., we show that: (i) The new heuristics correct the opportunity cost overestimation problem leading to higher quality policies, and (ii) By allowing for a systematic tradeoff of solution quality for time, the VFP algorithm runs much faster than the OC-DEC-MDP algorithm
2.
MOTIVATING EXAMPLE
3.
MODEL DESCRIPTION
The Sixth Intl. .
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4.
SOLUTION TECHNIQUES
4.1 Optimal Algorithms
4.2 Locally Optimal Algorithms
5.
VALUE FUNCTION PROPAGATION (VFP)
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5.1 Value Function Propagation Phase
0 otherwise
5.2 Reading the Policy
5.3 Probability Function Propagation Phase
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5.4 The Algorithm
5.5 Implementation of Function Operations
6.
SPLITTING THE OPPORTUNITY COST FUNCTIONS
834 The Sixth Intl. .
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7.
EXPERIMENTAL EVALUATION
836 The Sixth Intl. .
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8.
CONCLUSIONS
Decentralized Markov Decision Process (DEC-MDP) has been very popular for modeling of agent-coordination problems, it is very difficult to solve, especially for the real-world domains.
In this paper, we improved a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to large DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), provided two orthogonal improvements of OC-DEC-MDP: (i) It speeded up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each method rather than a separate value for each pair of method and time interval, and (ii) it achieved better solution qualities than OC-DEC-MDP because it corrected the overestimation of the opportunity cost of OC-DEC-MDP.
In terms of related work, we have extensively discussed the OCDEC-MDP algorithm [4].
Furthermore, as discussed in Section 4, there are globally optimal algorithms for solving DEC-MDPs with temporal constraints [1] [11].
Unfortunately, they fail to scaleup to large-scale domains at present time.
Beyond OC-DEC-MDP, there are other locally optimal algorithms for DEC-MDPs and DECPOMDPs [8] [12], [13], yet, they have traditionally not dealt with uncertain execution times and temporal constraints.
Finally, value function techniques have been studied in context of single agent MDPs [7] [9].
However, similarly to [6], they fail to address the lack of global state knowledge, which is a fundamental issue in decentralized planning.

On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to larger DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP.
First, it speeds up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each state (as a function of time) rather than a separate value for each pair of sate and time interval.
Furthermore, it achieves better solution qualities than OC-DEC-MDP because, as our analytical results show, it does not overestimate the expected total reward like OC-DEC - MDP.
We test both improvements independently in a crisis-management domain as well as for other types of domains.
Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution qualities in a variety of situations.
1.
INTRODUCTION
ration Rovers [2].
Because of the uncertain and dynamic characteristics of such domains, decision-theoretic models have received a lot of attention in recent years, mainly thanks to their expressiveness and the ability to reason about the utility of actions over time.
Key decision-theoretic models that have become popular in the literature include Decentralized Markov Decision Processes (DECMDPs) and Decentralized, Partially Observable Markov Decision Processes (DEC-POMDPs).
Although globally optimal algorithms for these subclasses have demonstrated promising results, domains on which these algorithms run are still small and time horizons are limited to only a few time ticks.
To remedy that, locally optimal algorithms have been proposed [12] [4] [5].
In particular, Opportunity Cost DEC-MDP [4] [5], referred to as OC-DEC-MDP, is particularly notable, as it has been shown to scale up to domains with hundreds of tasks and double digit time horizons.
Additionally, OC-DEC-MDP is unique in its ability to address both temporal constraints and uncertain method execution durations, which is an important factor for real-world domains.
OC-DEC-MDP is able to scale up to such domains mainly because instead of searching for the globally optimal solution, it carries out a series of policy iterations; in each iteration it performs a value iteration that reuses the data computed during the previous policy iteration.
However, OC-DEC-MDP is still slow, especially as the time horizon and the number of methods approach large values.
The reason for high runtimes of OC-DEC-MDP for such domains is a consequence of its huge state space, i.e., OC-DEC-MDP introduces a separate state for each possible pair of method and method execution interval.
Furthermore, OC-DEC-MDP overestimates the reward that a method expects to receive for enabling the execution of future methods.
This reward, also referred to as the opportunity cost, plays a crucial role in agent decision making, and as we show later, its overestimation leads to highly suboptimal policies.
In this context, we present VFP (= Value Function P ropagation), an efficient solution technique for the DEC-MDP model with temporal constraints and uncertain method execution durations, that builds on the success of OC-DEC-MDP.
VFP introduces our two orthogonal ideas: First, similarly to [7] [9] and [10], we maintain
and manipulate a value function over time for each method rather than a separate value for each pair of method and time interval.
Such representation allows us to group the time points for which the value function changes at the same rate (= its slope is constant), which results in fast, functional propagation of value functions.
Second, we prove (both theoretically and empirically) that OC-DEC - MDP overestimates the opportunity cost, and to remedy that, we introduce a set of heuristics, that correct the opportunity cost overestimation problem.
In section 3 we provide a detailed description of our DEC-MDP model with Temporal Constraints and in section 4 we discuss how one could solve the problems encoded in our model using globally optimal and locally optimal solvers.
Sections 5 and 6 discuss the two orthogonal improvements to the state-of-the-art OC-DEC-MDP algorithm that our VFP algorithm implements.
Finally, in section 7 we demonstrate empirically the impact of our two orthogonal improvements, i.e., we show that: (i) The new heuristics correct the opportunity cost overestimation problem leading to higher quality policies, and (ii) By allowing for a systematic tradeoff of solution quality for time, the VFP algorithm runs much faster than the OC-DEC-MDP algorithm
8.
CONCLUSIONS
Decentralized Markov Decision Process (DEC-MDP) has been very popular for modeling of agent-coordination problems, it is very difficult to solve, especially for the real-world domains.
In this paper, we improved a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to large DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), provided two orthogonal improvements of OC-DEC-MDP: (i) It speeded up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each method rather than a separate value for each pair of method and time interval, and (ii) it achieved better solution qualities than OC-DEC-MDP because it corrected the overestimation of the opportunity cost of OC-DEC-MDP.
In terms of related work, we have extensively discussed the OCDEC-MDP algorithm [4].
Furthermore, as discussed in Section 4, there are globally optimal algorithms for solving DEC-MDPs with temporal constraints [1] [11].
Unfortunately, they fail to scaleup to large-scale domains at present time.
Beyond OC-DEC-MDP, there are other locally optimal algorithms for DEC-MDPs and DECPOMDPs [8] [12], [13], yet, they have traditionally not dealt with uncertain execution times and temporal constraints.
Finally, value function techniques have been studied in context of single agent MDPs [7] [9].

On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to larger DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), combines two orthogonal improvements of OC-DEC-MDP.
First, it speeds up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each state (as a function of time) rather than a separate value for each pair of sate and time interval.
Furthermore, it achieves better solution qualities than OC-DEC-MDP because, as our analytical results show, it does not overestimate the expected total reward like OC-DEC - MDP.
We test both improvements independently in a crisis-management domain as well as for other types of domains.
Our experimental results demonstrate a significant speedup of VFP over OC-DEC-MDP as well as higher solution qualities in a variety of situations.
1.
INTRODUCTION
The development of algorithms for effective coordination of multiple agents acting as a team in uncertain and time critical domains has recently become a very active research field with potential applications ranging from coordination of agents during a hostage rescue mission [11] to the coordination of Autonomous Mars Explo
ration Rovers [2].
Because of the uncertain and dynamic characteristics of such domains, decision-theoretic models have received a lot of attention in recent years, mainly thanks to their expressiveness and the ability to reason about the utility of actions over time.
Key decision-theoretic models that have become popular in the literature include Decentralized Markov Decision Processes (DECMDPs) and Decentralized, Partially Observable Markov Decision Processes (DEC-POMDPs).
Unfortunately, solving these models optimally has been proven to be NEXP-complete [3], hence more tractable subclasses of these models have been the subject of intensive research.
In particular, Network Distributed POMDP [13] which assume that not all the agents interact with each other, Transition Independent DEC-MDP [2] which assume that transition function is decomposable into local transition functions or DEC-MDP with Event Driven Interactions [1] which assume that interactions between agents happen at fixed time points constitute good examples of such subclasses.
Although globally optimal algorithms for these subclasses have demonstrated promising results, domains on which these algorithms run are still small and time horizons are limited to only a few time ticks.
To remedy that, locally optimal algorithms have been proposed [12] [4] [5].
In particular, Opportunity Cost DEC-MDP [4] [5], referred to as OC-DEC-MDP, is particularly notable, as it has been shown to scale up to domains with hundreds of tasks and double digit time horizons.
Additionally, OC-DEC-MDP is unique in its ability to address both temporal constraints and uncertain method execution durations, which is an important factor for real-world domains.
OC-DEC-MDP is able to scale up to such domains mainly because instead of searching for the globally optimal solution, it carries out a series of policy iterations; in each iteration it performs a value iteration that reuses the data computed during the previous policy iteration.
However, OC-DEC-MDP is still slow, especially as the time horizon and the number of methods approach large values.
The reason for high runtimes of OC-DEC-MDP for such domains is a consequence of its huge state space, i.e., OC-DEC-MDP introduces a separate state for each possible pair of method and method execution interval.
Furthermore, OC-DEC-MDP overestimates the reward that a method expects to receive for enabling the execution of future methods.
This reward, also referred to as the opportunity cost, plays a crucial role in agent decision making, and as we show later, its overestimation leads to highly suboptimal policies.
In this context, we present VFP (= Value Function P ropagation), an efficient solution technique for the DEC-MDP model with temporal constraints and uncertain method execution durations, that builds on the success of OC-DEC-MDP.
VFP introduces our two orthogonal ideas: First, similarly to [7] [9] and [10], we maintain
and manipulate a value function over time for each method rather than a separate value for each pair of method and time interval.
Such representation allows us to group the time points for which the value function changes at the same rate (= its slope is constant), which results in fast, functional propagation of value functions.
Second, we prove (both theoretically and empirically) that OC-DEC - MDP overestimates the opportunity cost, and to remedy that, we introduce a set of heuristics, that correct the opportunity cost overestimation problem.
This paper is organized as follows: In section 2 we motivate this research by introducing a civilian rescue domain where a team of fire - brigades must coordinate in order to rescue civilians trapped in a burning building.
In section 3 we provide a detailed description of our DEC-MDP model with Temporal Constraints and in section 4 we discuss how one could solve the problems encoded in our model using globally optimal and locally optimal solvers.
Sections 5 and 6 discuss the two orthogonal improvements to the state-of-the-art OC-DEC-MDP algorithm that our VFP algorithm implements.
Finally, in section 7 we demonstrate empirically the impact of our two orthogonal improvements, i.e., we show that: (i) The new heuristics correct the opportunity cost overestimation problem leading to higher quality policies, and (ii) By allowing for a systematic tradeoff of solution quality for time, the VFP algorithm runs much faster than the OC-DEC-MDP algorithm
2.
MOTIVATING EXAMPLE
We are interested in domains where multiple agents must coordinate their plans over time, despite uncertainty in plan execution duration and outcome.
One example domain is large-scale disaster, like a fire in a skyscraper.
Because there can be hundreds of civilians scattered across numerous floors, multiple rescue teams have to be dispatched, and radio communication channels can quickly get saturated and useless.
In particular, small teams of fire-brigades must be sent on separate missions to rescue the civilians trapped in dozens of different locations.
Picture a small mission plan from Figure (1), where three firebrigades have been assigned a task to rescue the civilians trapped at site B, accessed from site A (e.g. an office accessed from the floor) 1.
General fire fighting procedures involve both: (i) putting out the flames, and (ii) ventilating the site to let the toxic, high temperature gases escape, with the restriction that ventilation should not be performed too fast in order to prevent the fire from spreading.
The team estimates that the civilians have 20 minutes before the fire at site B becomes unbearable, and that the fire at site A has to be put out in order to open the access to site B.
As has happened in the past in large scale disasters, communication often breaks down; and hence we assume in this domain that there is no communication between the fire-brigades 1,2 and 3 (denoted as FB1, FB2 and FB3).
Consequently, FB2 does not know if it is already safe to ventilate site A, FB1 does not know if it is already safe to enter site A and start fighting fire at site B, etc. .
We assign the reward 50 for evacuating the civilians from site B, and a smaller reward 20 for the successful ventilation of site A, since the civilians themselves might succeed in breaking out from site B.
One can clearly see the dilemma, that FB2 faces: It can only estimate the durations of the "Fight fire at site A" methods to be executed by FB1 and FB3, and at the same time FB2 knows that time is running out for civilians.
If FB2 ventilates site A too early, the fire will spread out of control, whereas if FB2 waits with the ventilation method for too long, fire at site B will become unbearable for the civilians.
In general, agents have to perform a sequence of such
Figure 1: Civilian rescue domain and a mission plan.
Dotted arrows represent implicit precedence constraints within an agent.
difficult decisions; in particular, decision process of FB2 involves first choosing when to start ventilating site A, and then (depending on the time it took to ventilate site A), choosing when to start evacuating the civilians from site B.
Such sequence of decisions constitutes the policy of an agent, and it must be found fast because time is running out.
3.
MODEL DESCRIPTION
We encode our decision problems in a model which we refer to as Decentralized MDP with Temporal Constraints 2.
Each instance of our decision problems can be described as a tuple (M, A, C, P, R) where M = {mi} | M | i = 1 is the set of methods, and A = {Ak} | A | k = 1 is the set of agents.
Agents cannot communicate during mission execution.
Each agent Ak is assigned to a set Mk of methods, such that S | A | k = 1 Mk = M and b' i, j; i ~ = jMi fl Mj = ø.
Also, each method of agent Ak can be executed only once, and agent Ak can execute only one method at a time.
Method execution times are uncertain and P = {pi} | M | i = 1 is the set of distributions of method execution durations.
In particular, pi (t) is the probability that the execution of method mi consumes time t. C is a set of temporal constraints in the system.
Methods are partially ordered and each method has fixed time windows inside which it can be executed, i.e., C = C ≺ U C [] where C ≺ is the set of predecessor constraints and C [] is the set of time window constraints.
For c E C ≺, c = (mi, mj) means that method mi precedes method mj i.e., execution of mj cannot start before mi terminates.
In particular, for an agent Ak, all its methods form a chain linked by predecessor constraints.
We assume, that the graph G = (M, C ≺) is acyclic, does not have disconnected nodes (the problem cannot be decomposed into independent subproblems), and its source and sink vertices identify the source and sink methods of the system.
For c E C [], c = (mi, EST, LET) means that execution of mi can only start after the Earliest Starting Time EST and must finish before the Latest End Time LET; we allow methods to have multiple disjoint time window constraints.
Although distributions pi can extend to infinite time horizons, given the time window constraints, the planning horizon Δ = max ~ m, τ, τ ~ ∈ C [] τ is considered as the mission deadline.
Finally, R = {ri} | M | i = 1 is the set of non-negative rewards, i.e., ri is obtained upon successful execution of mi.
Since there is no communication allowed, an agent can only estimate the probabilities that its methods have already been enabled 2One could also use the OC-DEC-MDP framework, which models both time and resource constraints
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by other agents.
Consequently, if mj ∈ Mk is the next method to be executed by the agent Ak and the current time is t ∈ [0, Δ], the agent has to make a decision whether to Execute the method mj (denoted as E), or to Wait (denoted as W).
In case agent Ak decides to wait, it remains idle for an arbitrary small time ~, and resumes operation at the same place (= about to execute method mj) at time t + ~.
In case agent Ak decides to Execute the next method, two outcomes are possible: Success: The agent Ak receives reward rj and moves on to its next method (if such method exists) so long as the following conditions hold: (i) All the methods {mi | ~ mi, mj ~ ∈ C ≺} that directly enable method mj have already been completed, (ii) Execution of method mj started in some time window of method mj, i.e., ∃ ~ mj, τ, τ, ~ ∈ C [] such that t ∈ [τ, τ ~], and (iii) Execution of method mj finished inside the same time window, i.e., agent Ak completed method mj in time less than or equal to τ ~ − t. Failure: If any of the above-mentioned conditions does not hold, agent Ak stops its execution.
Other agents may continue their execution, but methods mk ∈ {m | ~ mj, m ~ ∈ C ≺} will never become enabled.
The policy πk of an agent Ak is a function πk: Mk × [0, Δ] → {W, E}, and πk (~ m, t ~) = a means, that if Ak is at method m at time t, it will choose to perform the action a.
A joint policy π = [πk] | A | k = 1 is considered to be optimal (denoted as π ∗), if it maximizes the sum of expected rewards for all the agents.
4.
SOLUTION TECHNIQUES
4.1 Optimal Algorithms
Optimal joint policy π ∗ is usually found by using the Bellman update principle, i.e., in order to determine the optimal policy for method mj, optimal policies for methods mk ∈ {m | ~ mj, m ~ ∈ C ≺} are used.
Unfortunately, for our model, the optimal policy for method mj also depends on policies for methods mi ∈ {m | ~ m, mj ~ ∈ C ≺}.
This double dependency results from the fact, that the expected reward for starting the execution of method mj at time t also depends on the probability that method mj will be enabled by time t. Consequently, if time is discretized, one needs to consider Δ | M | candidate policies in order to find π ∗.
Thus, globally optimal algorithms used for solving real-world problems are unlikely to terminate in reasonable time [11].
The complexity of our model could be reduced if we considered its more restricted version; in particular, if each method mj was allowed to be enabled at time points t ∈ Tj ⊂ [0, Δ], the Coverage Set Algorithm (CSA) [1] could be used.
However, CSA complexity is double exponential in the size of Ti, and for our domains Tj can store all values ranging from 0 to Δ.
4.2 Locally Optimal Algorithms
Following the limited applicability of globally optimal algorithms for DEC-MDPs with Temporal Constraints, locally optimal algorithms appear more promising.
Specially, the OC-DEC-MDP algorithm [4] is particularly significant, as it has shown to easily scale up to domains with hundreds of methods.
The idea of the OC-DECMDP algorithm is to start with the earliest starting time policy π0 (according to which an agent will start executing the method m as soon as m has a non-zero chance of being already enabled), and then improve it iteratively, until no further improvement is possible.
At each iteration, the algorithm starts with some policy π, which uniquely determines the probabilities Pi, [τ, τ,] that method mi will be performed in the time interval [τ, τ ~].
It then performs two steps: Step 1: It propagates from sink methods to source methods the values Vi, [τ, τ,], that represent the expected utility for executing method mi in the time interval [τ, τ ~].
This propagation uses the probabilities Pi, [τ, τ,] from previous algorithm iteration.
We call this step a value propagation phase.
Step 2: Given the values Vi, [τ, τ,] from Step 1, the algorithm chooses the most profitable method execution intervals which are stored in a new policy π ~.
It then propagates the new probabilities Pi, [τ, τ,] from source methods to sink methods.
We call this step a probability propagation phase.
If policy π ~ does not improve π, the algorithm terminates.
There are two shortcomings of the OC-DEC-MDP algorithm that we address in this paper.
First, each of OC-DEC-MDP states is a pair ~ mj, [τ, τ ~] ~, where [τ, τ ~] is a time interval in which method mj can be executed.
While such state representation is beneficial, in that the problem can be solved with a standard value iteration algorithm, it blurs the intuitive mapping from time t to the expected total reward for starting the execution of mj at time t. Consequently, if some method mi enables method mj, and the values Vj, [τ, τ,] ∀ τ, τ, ∈ [0, Δ] are known, the operation that calculates the values Vi, [τ, τ,] ∀ τ, τ ~ ∈ [0, Δ] (during the value propagation phase), runs in time O (I2), where I is the number of time intervals 3.
Since the runtime of the whole algorithm is proportional to the runtime of this operation, especially for big time horizons Δ, the OC - DECMDP algorithm runs slow.
Second, while OC-DEC-MDP emphasizes on precise calculation of values Vj, [τ, τ,], it fails to address a critical issue that determines how the values Vj, [τ, τ,] are split given that the method mj has multiple enabling methods.
As we show later, OC-DEC-MDP splits Vj, [τ, τ,] into parts that may overestimate Vj, [τ, τ,] when summed up again.
As a result, methods that precede the method mj overestimate the value for enabling mj which, as we show later, can have disastrous consequences.
In the next two sections, we address both of these shortcomings.
5.
VALUE FUNCTION PROPAGATION (VFP)
The general scheme of the VFP algorithm is identical to the OCDEC-MDP algorithm, in that it performs a series of policy improvement iterations, each one involving a Value and Probability Propagation Phase.
However, instead of propagating separate values, VFP maintains and propagates the whole functions, we therefore refer to these phases as the value function propagation phase and the probability function propagation phase.
To this end, for each method mi ∈ M, we define three new functions: Value Function, denoted as vi (t), that maps time t ∈ [0, Δ] to the expected total reward for starting the execution of method mi at time t. Opportunity Cost Function, denoted as Vi (t), that maps time t ∈ [0, Δ] to the expected total reward for starting the execution of method mi at time t assuming that mi is enabled.
Probability Function, denoted as Pi (t), that maps time t ∈ [0, Δ] to the probability that method mi will be completed before time t.
Such functional representation allows us to easily read the current policy, i.e., if an agent Ak is at method mi at time t, then it will wait as long as value function vi (t) will be greater in the future.
Formally: j W if ∃ t,> t such that vi (t) <vi (t ~) πk (~ mi, t ~) = E otherwise.
We now develop an analytical technique for performing the value function and probability function propagation phases.
3Similarly for the probability propagation phase
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5.1 Value Function Propagation Phase
Suppose, that we are performing a value function propagation phase during which the value functions are propagated from the sink methods to the source methods.
At any time during this phase we encounter a situation shown in Figure 2, where opportunity cost functions [Vjn] Nn = 0 of methods [mjn] Nn = 0 are known, and the opportunity cost Vio of method mio is to be derived.
Let pio be the probability distribution function of method mio execution duration, and rio be the immediate reward for starting and completing the execution of method mio inside a time interval [τ, τ'] such that ~ mioτ, τ' ~ ∈ C [].
The function Vio is then derived from rio and opportunity costs Vjn, io (t) n = 1,..., N from future methods.
Formally:
0 otherwise
Note, that fort ∈ [τ, τ'], if h (t): = rio + 'N n = 0 Vjn, io (τ' − t) then Vio is a convolution of p and h: vio (t) = (pio ∗ h) (τ' − t).
Assume for now, that Vjn, io represents a full opportunity cost, postponing the discussion on different techniques for splitting the opportunity cost Vjo into [Vjo, ik] Kk = 0 until section 6.
We now show how to derive Vjo, io (derivation of Vjn, io for n = ~ 0 follows the same scheme).
Figure 2: Fragment of an MDP of agent Ak.
Probability functions propagate forward (left to right) whereas value functions propagate backward (right to left).
Let Vjo, io (t) be the opportunity cost of starting the execution of method mjo at time t given that method mio has been completed.
It is derived by multiplying Vio by the probability functions of all methods other than mio that enable mjo.
Formally: Knowing the opportunity cost Vio, we can then easily derive the value function vio.
Let Ak be an agent assigned to the method mio.
If Ak is about to start the execution of mio it means, that Ak must have completed its part of the mission plan up to the method mio.
Since Ak does not know if other agents have completed methods [mlk] k = K k = 1, in order to derive vio, it has to multiply Vio by the probability functions of all methods of other agents that enable mio.
Formally:
Where the dependency of [Plk] Kk = 1 is also ignored.
We have consequently shown a general scheme how to propagate the value functions: Knowing [vjn] Nn = 0 and [Vjn] Nn = 0 of methods [mjn] Nn = 0 we can derive vio and Vio of method mio.
In general, the value function propagation scheme starts with sink nodes.
It then visits at each time a method m, such that all the methods that m enables have already been marked as visited.
The value function propagation phase terminates when all the source methods have been marked as visited.
5.2 Reading the Policy
In order to determine the policy of agent Ak for the method mjo we must identify the set Zjo of intervals [z, z'] ⊂ [0,..., Δ], such that:
One can easily identify the intervals of Zjo by looking at the time intervals in which the value function vjo does not decrease monotonically.
5.3 Probability Function Propagation Phase
Assume now, that value functions and opportunity cost values have all been propagated from sink methods to source nodes and the sets Zj for all methods mj ∈ M have been identified.
Since value function propagation phase was using probabilities Pi (t) for methods mi ∈ M and times t ∈ [0, Δ] found at previous algorithm iteration, we now have to find new values Pi (t), in order to prepare the algorithm for its next iteration.
We now show how in the general case (Figure 2) propagate the probability functions forward through one method, i.e., we assume that the probability functions [Pik] Kk = 0 of methods [mik] Kk = 0 are known, and the probability function Pjo of method mjo must be derived.
Let pjo be the probability distribution function of method mjo execution duration, and Zjo be the set of intervals of inactivity for method mjo, found during the last value function propagation phase.
If we ignore the dependency of [Pik] Kk = 0 then the probability Pjo (t) that the execution of method mjo starts before time t is given by:
Where similarly to [4] and [5] we ignored the dependency of [Plk] K k = 1.
Observe that Vjo, io does not have to be monotonically decreasing, i.e., delaying the execution of the method mio can sometimes be profitable.
Therefore the opportunity cost Vjo, io (t) of enabling method mio at time t must be greater than or equal to Vjo, io.
Furthermore, Vjo, io should be non-increasing.
Formally:
We have consequently shown how to propagate the probability functions [Pik] Kk = 0 of methods [mik] Kk = 0 to obtain the probability function Pjo of method mjo.
The general, the probability function propagation phase starts with source methods msi for which we know that Psi = 1 since they are enabled by default.
We then visit at each time a method m such that all the methods that enable
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m have already been marked as visited.
The probability function propagation phase terminates when all the sink methods have been marked as visited.
5.4 The Algorithm
Similarly to the OC-DEC-MDP algorithm, VFP starts the policy improvement iterations with the earliest starting time policy π0.
Then at each iteration it: (i) Propagates the value functions [vi] | M | i = 1 using the old probability functions [Pi] | M | i = 1 from previous algorithm iteration and establishes the new sets [Zi] | M | i = 1 of method inactivity intervals, and (ii) propagates the new probability functions [Pi ~] | M | i = 1 using the newly established sets [Zi] | M | i = 1.
These new functions [Pi ~] | M | i = 1 are then used in the next iteration of the algorithm.
Similarly to OC-DEC-MDP, VFP terminates if a new policy does not improve the policy from the previous algorithm iteration.
5.5 Implementation of Function Operations
So far, we have derived the functional operations for value function and probability function propagation without choosing any function representation.
In general, our functional operations can handle continuous time, and one has freedom to choose a desired function approximation technique, such as piecewise linear [7] or piecewise constant [9] approximation.
However, since one of our goals is to compare VFP with the existing OC-DEC - MDP algorithm, that works only for discrete time, we also discretize time, and choose to approximate value functions and probability functions with piecewise linear (PWL) functions.
When the VFP algorithm propagates the value functions and probability functions, it constantly carries out operations represented by equations (1) and (3) and we have already shown that these operations are convolutions of some functions p (t) and h (t).
If time is discretized, functions p (t) and h (t) are discrete; however, h (t) can be nicely approximated with a PWL function bh (t), which is exactly what VFP does.
As a result, instead of performing O (Δ2) multiplications to compute f (t), VFP only needs to perform O (k · Δ) multiplications to compute f (t), where k is the number of linear segments of bh (t) (note, that since h (t) is monotonic, bh (t) is usually close to h (t) with k ~ Δ).
Since Pi values are in range [0, 1] and Vi values are in range [0, Pmi ∈ M ri], we suggest to approximate Vi (t) with bVi (t) within error ~ V, and Pi (t) with bPi (t) within error ~ P.
We now prove that the overall approximation error accumulated during the value function propagation phase can be expressed in terms of ~ P and ~ V: THEOREM 1.
Let C ≺ be a set of precedence constraints of a DEC-MDP with Temporal Constraints, and ~ P and ~ V be the probability function and value function approximation errors respectively.
The overall error ~ π = maxV supt ∈ [0, Δ] | V (t) − Vb (t) | of value function propagation phase is then bounded by:
PROOF.
In order to establish the bound for ~ π, we first prove by induction on the size of C ≺, that the overall error of probability function propagation phase, ~ π (P) = maxP supt ∈ [0, Δ] | P (t) − Pb (t) | is bounded by (1 + ~ P) | C ≺ | − 1.
Induction base: If n = 1 only two methods are present, and we will perform the operation identified by Equation (3) only once, introducing the error ~ π (P) = ~ P = (1 + ~ P) | C ≺ | − 1.
Induction step: Suppose, that ~ π (P) for | C ≺ | = n is bounded by (1 + ~ P) n − 1, and we want to prove that this statement holds for | C ≺ | = n. Let G = ~ M, C ≺ ~ be a graph with at most n + 1 edges, and G = ~ M, C ≺ ~ be a subgraph of G, such that C ≺ = C ≺ − {~ mi, mj ~}, where mj ∈ M is a sink node in G. From the induction assumption we have, that C ≺ introduces the probability propagation phase error bounded by (1 + ~ P) n − 1.
We now add back the link {~ mi, mj ~} to C ≺, which affects the error of only one probability function, namely Pj, by a factor of (1 + ~ P).
Since probability propagation phase error in C ≺ was bounded by (1 + ~ P) n − 1, in C ≺ = C ≺ ∪ {~ mi, mj ~} it can be at most ((1 + functions are not overestimated, they are bounded by P
and the error of a single value function propagation operation will be at most
6.
SPLITTING THE OPPORTUNITY COST FUNCTIONS
In section 5 we left out the discussion about how the opportunity cost function Vjo of method mjo is split into opportunity cost functions [Vjo, ik] Kk = 0 sent back to methods [mik] Kk = 0, that directly enable method mjo.
So far, we have taken the same approach as in [4] and [5] in that the opportunity cost function Vjo, ik that the method mik sends back to the method mjo is a minimal, non-increasing function that dominates function Vjo, ik (t) = (Vjo · Qk ~ ∈ {0,..., K} Pik ~) (t).
We refer to this approach, as heurisk ~ ~ = k tic H ~ 1,1 ~.
Before we prove that this heuristic overestimates the opportunity cost, we discuss three problems that might occur when splitting the opportunity cost functions: (i) overestimation, (ii) underestimation and (iii) starvation.
Consider the situation in Figure
Figure 3: Splitting the value function of method mjo among methods [mik] Kk = 0.
(3) when value function propagation for methods [mik] Kk = 0 is performed.
For each k = 0,..., K, Equation (1) derives the opportunity cost function Vik from immediate reward rk and opportunity cost function Vjo, ik.
If m0 is the only methods that precedes method mk, then Vik,0 = Vik is propagated to method m0, and consequently the opportunity cost for completing the method m0 at time t is equal to PK k = 0 Vik,0 (t).
If this cost is overestimated, then an agent A0 at method m0 will have too much incentive to finish the execution of m0 at time t. Consequently, although the probability P (t) that m0 will be enabled by other agents by time t is low, agent A0 might still find the expected utility of starting the execution of m0 at time t higher than the expected utility of doing it later.
As a result, it will choose at time t to start executing method m0 instead of waiting, which can have disastrous consequences.
Similarly, if PKk = 0 Vik,0 (t) is underestimated, agent A0 might loose interest in enabling the future methods [mik] Kk = 0 and just focus on
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maximizing the chance of obtaining its immediate reward r0.
Since this chance is increased when agent A0 waits4, it will consider at time t to be more profitable to wait, instead of starting the execution of m0, which can have similarly disastrous consequences.
Finally, if Vj0 is split in a way, that for some k, Vj0, ik = 0, it is the method mik that underestimates the opportunity cost of enabling method mj0, and the similar reasoning applies.
We call such problem a starvation of method mk.
That short discussion shows the importance of splitting the opportunity cost function Vj0 in such a way, that overestimation, underestimation, and starvation problem is avoided.
We now prove that:
Consequently, the opportunity cost PKk = 0 Vik (t0) of starting the execution of methods [mik] Kk = 0 at time t E [0,.
.
, Δ] is greater than the opportunity cost Vj0 (t0) which proves the theorem.Figure 4 shows that the overestimation of opportunity cost is easily observable in practice.
To remedy the problem of opportunity cost overestimation, we propose three alternative heuristics that split the opportunity cost functions:
• Heuristic H ~ 1,0 ~: Only one method, mik gets the full expected reward for enabling method mj0, i.e., Vj0, ik ~ (t) = 0 • Heuristic bH ~ 1,1 ~: This is a normalized version of the H ~ 1,1 ~ heuristic in that each method [mik] Kk = 0 initially gets the full opportunity cost for enabling the method mj0.
To avoid opportunity cost overestimation, we normalize the split functions when their sum exceeds the opportunity cost function to be split.
Formally:
For the new heuristics, we now prove, that:
For heuristic bH ~ 1,1 ~, the opportunity cost function Vj0 is by definition split in such manner, that PKk = 0 Vik (t) <Vj0 (t).
Consequently, we have proved, that our new heuristics H ~ 1,0 ~, H ~ 1/2,1 / 2 ~ and bH ~ 1,1 ~ avoid the overestimation of the opportunity cost.
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The reason why we have introduced all three new heuristics is the following: Since H (1,1) overestimates the opportunity cost, one has to choose which method mik will receive the reward from enabling the method mj0, which is exactly what the heuristic H (1,0) does.
However, heuristic H (1,0) leaves K − 1 methods that precede the method mj0 without any reward which leads to starvation.
Starvation can be avoided if opportunity cost functions are split using heuristic H (1/2,1 / 2), that provides reward to all enabling methods.
However, the sum of split opportunity cost functions for the H (1/2,1 / 2) heuristic can be smaller than the non-zero split opportunity cost function for the H (1,0) heuristic, which is clearly undesirable.
Such situation (Figure 4, heuristic H (1,0)) occurs because the mean f + g 2 of two functions f, g is not smaller than f nor g only if f = g.
This is why we have proposed the bH (1,1) heuristic, which by definition avoids the overestimation, underestimation and starvation problems.
7.
EXPERIMENTAL EVALUATION
Since the VFP algorithm that we introduced provides two orthogonal improvements over the OC-DEC-MDP algorithm, the experimental evaluation we performed consisted of two parts: In part 1, we tested empirically the quality of solutions that an locally optimal solver (either OC-DEC-MDP or VFP) finds, given it uses different opportunity cost function splitting heuristic, and in part 2, we compared the runtimes of the VFP and OC-DEC - MDP algorithms for a variety of mission plan configurations.
Part 1: We first ran the VFP algorithm on a generic mission plan configuration from Figure 3 where only methods mj0, mi1, mi2 and m0 were present.
Time windows of all methods were set to 400, duration pj0 of method mj0 was uniform, i.e., pj0 (t) = 1
and durations pi1, pi2 of methods mi1, mi2 were normal distributions, i.e., pi1 = N (μ = 250, σ = 20), and pi2 = N (μ = 200, σ = 100).
We assumed that only method mj0 provided reward, i.e. rj0 = 10 was the reward for finishing the execution of method mj0 before time t = 400.
We show our results in Figure (4) where the x-axis of each of the graphs represents time whereas the y-axis represents the opportunity cost.
The first graph confirms, that when the opportunity cost function Vj0 was split into opportunity cost functions Vi1 and Vi2 using the H (1,1) heuristic, the function Vi1 + Vi2 was not always below the Vj0 function.
In particular, Vi1 (280) + Vi2 (280) exceeded Vj0 (280) by 69%.
When heuristics H (1,0), H (1/2,1 / 2) and bH (1,1) were used (graphs 2,3 and 4), the function Vi1 + Vi2 was always below Vj0.
We then shifted our attention to the civilian rescue domain introduced in Figure 1 for which we sampled all action execution durations from the normal distribution N = (μ = 5, σ = 2)).
To obtain the baseline for the heuristic performance, we implemented a globally optimal solver, that found a true expected total reward for this domain (Figure (6a)).
We then compared this reward with a expected total reward found by a locally optimal solver guided by each of the discussed heuristics.
Figure (6a), which plots on the y-axis the expected total reward of a policy complements our previous results: H (1,1) heuristic overestimated the expected total reward by 280% whereas the other heuristics were able to guide the locally optimal solver close to a true expected total reward.
Part 2: We then chose H (1,1) to split the opportunity cost functions and conducted a series of experiments aimed at testing the scalability of VFP for various mission plan configurations, using the performance of the OC-DEC-MDP algorithm as a benchmark.
We began the VFP scalability tests with a configuration from Figure (5a) associated with the civilian rescue domain, for which method execution durations were extended to normal distributions N (μ =
Figure 5: Mission plan configurations: (a) civilian rescue domain, (b) chain of n methods, (c) tree of n methods with branching factor = 3 and (d) square mesh of n methods.
Figure 6: VFP performance in the civilian rescue domain.
30, σ = 5), and the deadline was extended to Δ = 200.
We decided to test the runtime of the VFP algorithm running with three different levels of accuracy, i.e., different approximation parameters ~ P and ~ V were chosen, such that the cumulative error of the solution found by VFP stayed within 1%, 5% and 10% of the solution found by the OC - DEC-MDP algorithm.
We then run both algorithms for a total of 100 policy improvement iterations.
Figure (6b) shows the performance of the VFP algorithm in the civilian rescue domain (y-axis shows the runtime in milliseconds).
As we see, for this small domain, VFP runs 15% faster than OCDEC-MDP when computing the policy with an error of less than 1%.
For comparison, the globally optimal solved did not terminate within the first three hours of its runtime which shows the strength of the opportunistic solvers, like OC-DEC-MDP.
We next decided to test how VFP performs in a more difficult domain, i.e., with methods forming a long chain (Figure (5b)).
We tested chains of 10, 20 and 30 methods, increasing at the same time method time windows to 350, 700 and 1050 to ensure that later methods can be reached.
We show the results in Figure (7a), where we vary on the x-axis the number of methods and plot on the y-axis the algorithm runtime (notice the logarithmic scale).
As we observe, scaling up the domain reveals the high performance of VFP: Within 1% error, it runs up to 6 times faster than OC-DECMDP.
We then tested how VFP scales up, given that the methods are arranged into a tree (Figure (5c)).
In particular, we considered trees with branching factor of 3, and depth of 2, 3 and 4, increasing at the same time the time horizon from 200 to 300, and then to 400.
We show the results in Figure (7b).
Although the speedups are smaller than in case of a chain, the VFP algorithm still runs up to 4 times faster than OC-DEC-MDP when computing the policy with an error of less than 1%.
We finally tested how VFP handles the domains with methods arranged into a n × n mesh, i.e., C _ = {(mi, j, mk, j +1)} for i = 1,..., n; k = 1,..., n; j = 1,..., n − 1.
In particular, we consider
836 The Sixth Intl. .
Joint Conf.
on Autonomous Agents and Multi-Agent Systems (AAMAS 07)
Figure 4: Visualization of heuristics for opportunity costs splitting.
Figure 7: Scalability experiments for OC-DEC-MDP and VFP for different network configurations.
meshes of 3 × 3, 4 × 4, and 5 × 5 methods.
For such configurations we have to greatly increase the time horizon since the probabilities of enabling the final methods by a particular time decrease exponentially.
We therefore vary the time horizons from 3000 to 4000, and then to 5000.
We show the results in Figure (7c) where, especially for larger meshes, the VFP algorithm runs up to one order of magnitude faster than OC-DEC-MDP while finding a policy that is within less than 1% from the policy found by OC - DECMDP.
8.
CONCLUSIONS
Decentralized Markov Decision Process (DEC-MDP) has been very popular for modeling of agent-coordination problems, it is very difficult to solve, especially for the real-world domains.
In this paper, we improved a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scale up to large DEC-MDPs.
Our heuristic solution method, called Value Function Propagation (VFP), provided two orthogonal improvements of OC-DEC-MDP: (i) It speeded up OC-DECMDP by an order of magnitude by maintaining and manipulating a value function for each method rather than a separate value for each pair of method and time interval, and (ii) it achieved better solution qualities than OC-DEC-MDP because it corrected the overestimation of the opportunity cost of OC-DEC-MDP.
In terms of related work, we have extensively discussed the OCDEC-MDP algorithm [4].
Furthermore, as discussed in Section 4, there are globally optimal algorithms for solving DEC-MDPs with temporal constraints [1] [11].
Unfortunately, they fail to scaleup to large-scale domains at present time.
Beyond OC-DEC-MDP, there are other locally optimal algorithms for DEC-MDPs and DECPOMDPs [8] [12], [13], yet, they have traditionally not dealt with uncertain execution times and temporal constraints.
Finally, value function techniques have been studied in context of single agent MDPs [7] [9].
However, similarly to [6], they fail to address the lack of global state knowledge, which is a fundamental issue in decentralized planning.

decentr partial observ markov decis process