In many real world planning scenarios, agents often do not have enough resources to achieve all o... more In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Hence, this requires finding plans that satisfy only a subset of the them. Solving such partial satisfaction planning (PSP) problems poses several challenges, in-cluding an increased emphasis on modelling and han-dling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the plan-ning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BENE-FIT. We develop two techniques, one based on integer programming, called OptiPlan, and the other based on regression planning with reachability heuristics, called AltAltps. Our empirical studies with these two plan-ners show that the heuristic planner AltAltps generates plans that are quite close to the quality of plans gener-ated by OptiPlan, while inc...
Abstract Creating decision support systems to help people coordinate in the real world is difficu... more Abstract Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
In many real world planning scenarios, agents often do not have enough resources to achieve all o... more In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Hence, this requires finding plans that satisfy only a subset of the them. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modelling and handling plan quality (in terms of action costs and goal utilities).
We address the problem of creating visualizations to debug and understand multi-agent systems. Th... more We address the problem of creating visualizations to debug and understand multi-agent systems. The key challenges are that (1) needs arise dynamically, i.e., it is difficult to know a priori what visualizations one wants, (2) extensive exper- tise on the system, the algorithms and visualization tools are often needed for implementation, and (3) agents can be running in a distributed environment. We have developed VizScript, a collection of tools that expedites the process of creation. VizScript does not require great knowledge of the intricacies of internal data structures or display creation and is functional with data streams from multiple sources. We show that by combining generic instrumentation, a knowl- edge base, and simple scene definition primitives with a rea- soning system, we are able to recreate the visualizations for a complex multi-agent system with an order-of-magnitude less effort than was required in a Java implementation.
Workshop 25: Optimisation in Multi-agent Systems, May 10, 2010
Creating decision support systems to help people coordinate in the real world is difficult becaus... more Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
Creating decision support systems to help people coordinate in the real world is difficult becaus... more Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
The MSDM-06 Organizing Committee Ranjit Nair, Honeywell Laboratories Raphen Becker, University of Massachusetts, Amherst, 2006
We consider a team of agents that are required to coordinate their actions in order to maximize a... more We consider a team of agents that are required to coordinate their actions in order to maximize a global objective. Our domains are characterized by uncertainty, dynamism, and distributed information. Determining appropriate actions becomes quite difficult, especially as the number of agents and the coupling between them increases. This paper discusses four contributions toward the goal of coordinating agents under uncertainty in large-scale settings:(i) an approach based on identifying the criticality of various activities ...
In many real world planning scenarios, agents often do not have enough resources to achieve all o... more In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Hence, this requires finding plans that satisfy only a subset of the them. Solving such partial satisfaction planning (PSP) problems poses several challenges, in-cluding an increased emphasis on modelling and han-dling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the plan-ning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BENE-FIT. We develop two techniques, one based on integer programming, called OptiPlan, and the other based on regression planning with reachability heuristics, called AltAltps. Our empirical studies with these two plan-ners show that the heuristic planner AltAltps generates plans that are quite close to the quality of plans gener-ated by OptiPlan, while inc...
Abstract Creating decision support systems to help people coordinate in the real world is difficu... more Abstract Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
In many real world planning scenarios, agents often do not have enough resources to achieve all o... more In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Hence, this requires finding plans that satisfy only a subset of the them. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modelling and handling plan quality (in terms of action costs and goal utilities).
We address the problem of creating visualizations to debug and understand multi-agent systems. Th... more We address the problem of creating visualizations to debug and understand multi-agent systems. The key challenges are that (1) needs arise dynamically, i.e., it is difficult to know a priori what visualizations one wants, (2) extensive exper- tise on the system, the algorithms and visualization tools are often needed for implementation, and (3) agents can be running in a distributed environment. We have developed VizScript, a collection of tools that expedites the process of creation. VizScript does not require great knowledge of the intricacies of internal data structures or display creation and is functional with data streams from multiple sources. We show that by combining generic instrumentation, a knowl- edge base, and simple scene definition primitives with a rea- soning system, we are able to recreate the visualizations for a complex multi-agent system with an order-of-magnitude less effort than was required in a Java implementation.
Workshop 25: Optimisation in Multi-agent Systems, May 10, 2010
Creating decision support systems to help people coordinate in the real world is difficult becaus... more Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
Creating decision support systems to help people coordinate in the real world is difficult becaus... more Creating decision support systems to help people coordinate in the real world is difficult because it requires simultaneously addressing planning, scheduling, uncertainty and distribution. Generic AI approaches produce inadequate solutions because they cannot leverage the structure of domains and the intuition that end-users have for solving particular problem instances. We present a general approach where end-users can encode their intuition as guidance enabling the system to decompose large distributed problems into ...
The MSDM-06 Organizing Committee Ranjit Nair, Honeywell Laboratories Raphen Becker, University of Massachusetts, Amherst, 2006
We consider a team of agents that are required to coordinate their actions in order to maximize a... more We consider a team of agents that are required to coordinate their actions in order to maximize a global objective. Our domains are characterized by uncertainty, dynamism, and distributed information. Determining appropriate actions becomes quite difficult, especially as the number of agents and the coupling between them increases. This paper discusses four contributions toward the goal of coordinating agents under uncertainty in large-scale settings:(i) an approach based on identifying the criticality of various activities ...
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