Alfonso E. Gerevini - Academia.edu (original) (raw)

Papers by Alfonso E. Gerevini

The ParLPG planning system is based on the idea of using a generic algorithm configuration proced... more The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure-here, the well-known ParamILS framework-to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain. This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10 17 possible configurations. A recent, largescale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains.

PROCEEDINGS OF THE …, 2005

The ability to express "derived predicates" in the formaliza- tion of a planning domain... more The ability to express "derived predicates" in the formaliza- tion of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of "Rule-Action Graphs", particular graphs of actions and rules representing derived predicates. We present some techniques for representing rules and reasoning with them,

Future Internet

During the COVID-19 pandemic, the corporate online training sector has increased exponentially an... more During the COVID-19 pandemic, the corporate online training sector has increased exponentially and online course providers had to implement innovative solutions to be more efficient and provide a satisfactory service. This paper considers a real case study in implementing a chatbot, which answers frequently asked questions from learners on an Italian e-learning platform that provides workplace safety courses to several business customers. Having to respond quickly to the increase in the courses activated, the company decided to develop a chatbot using a cloud-based service currently available on the market. These services are based on Natural Language Understanding (NLU) engines, which deal with identifying information such as entities and intentions from the sentences provided as input. To integrate a chatbot in an e-learning platform, we studied the performance of the intent recognition task of the major NLU platforms available on the market with an in-depth comparison, using an I...

Discovering the effect of the simultaneous assumption of drugs is a very important field in medic... more Discovering the effect of the simultaneous assumption of drugs is a very important field in medical research that could improve the effectiveness of healthcare and avoid adverse drug reactions which can cause health problems to patients. Although there are several pharmacological databases containing information on drugs, this type of information is often expressed in the form of free text. Analyzing sentences in order to extract drug-drug interactions was the objective of the DDIExtraction-2013 task. Despite the fact that the challenge took place six years ago, the interest on this task is still active and several new methods based on Recurrent Neural Networks and Attention Mechanisms have been designed. In this paper, we propose a model that combines bidirectional Long Short Term Memory (LSTM) networks with the Self-Interaction Attention Mechanism. Experimental analysis shows how this model improves the classification accuracy reducing the tendency to predict the majority class re...

An estimated 20-25% of women experience sexual assault while at college. In response, institution... more An estimated 20-25% of women experience sexual assault while at college. In response, institutions of higher education are improving their policies and working to educate students on the issue. The purpose of this study is to examine whether undergraduate students at Duke University know and understand the University's Student Sexual Misconduct Policy on consent and sexual violence. Data gathered from student surveys (n = 320) yielded mixed results on respondents' knowledge of the policy. In addition, though students had greater understanding of sexual violence than hypothesized, respondents lacked understanding of the role of alcohol in consent. Statistical analyses showed that men, varsity athletes, freshmen and non-LGBTQ students were more likely to misunderstand sexual violence, as measured through responses to scenario questions on the student survey. Recommendations of this study to the University include adding information on alcohol and consent to the Student Sexual Misconduct Policy, improving outreach and follow up for educational programming, and implementing scenario questions in future surveys and training materials.

In multi-agent planning, preserving the agents' privacy has become an increasingly popular re... more In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is...

Fundamenta Informaticae, 2011

Planning through local search and action graphs is a powerful approach to fully-automated plannin... more Planning through local search and action graphs is a powerful approach to fully-automated planning which is implemented in the well-known LPG planner. The approach is based on a stochastic local search procedure exploring a space of partial plans and several heuristic features with different possible options. In this paper, we experimentally analyze the most important of them, with the goal of understanding and evaluating their impact on the performance of LPG, and of identifying default settings that work well on a large class of problems. In particular, we analyze several heuristic techniques for (a) evaluating the search neighborhood, (b) defining/restricting the search neighborhood, (c) selecting the next plan flaw to handle, (d) setting the “noise” parameter randomizing the search, and (e) computing reachability information that can be exploited by the heuristic functions used to evaluate the neighborhood elements. Some of these techniques were introduced in previous work on LPG, while others are new. Additional experimental results indicate that the current version of LPG using the identified best heuristic techniques as the default settings is competitive with the winner of the last (2008) International Planning Competition.

Journal of Artificial Intelligence Research, 2014

In the field of domain-independent planning, several powerful planners implementing different tec... more In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won t...

Journal of Artificial Intelligence Research, 1996

We propose some domain-independent techniques for bringing well-founded partial-order planners cl... more We propose some domain-independent techniques for bringing well-founded partial-order planners closer to practicality. The first two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by UCPOP to select plans for refinement. The other is based on preferring ``zero commitment'' (forced) plan refinements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the definitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modifications of UCPOP, our improved plan and...

Journal of Artificial Intelligence Research, 2006

The treatment of exogenous events in planning is practically important in many real-world domains... more The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When actions have durations, handling such temporal constraints adds an extra difficulty to planning. We propose an approach to planning in these domains which integrates constraint-based temporal reasoning into a graph-based planning framework using local search. Our techniques are implemented in a planner that took part in the 4th International Planning Competition (IPC-4). A statistical analysis of the results of IPC-4 demonstrates the effectiveness of our approach in terms of both CPU-time and plan quality. Additional experiments show the good performance of the temporal reasoning techniques int...

AI Communications, 2018

Solver competitions have been used in many areas of AI to assess the current state of the art and... more Solver competitions have been used in many areas of AI to assess the current state of the art and guide future research and development. AI planning is no exception, and the International Planning Competition (IPC) has been frequently run for nearly two decades. Due to the organisational and computational burden involved in running these competitions, solvers are generally compared using a single homogeneous hardware and software environment for all competitors. To what extent does the specific choice of hardware and software environment have an effect on solver performance, and is that effect distributed equally across the competing solvers? In this work, we use the competing planners and benchmark instance sets from the 2014 IPC to investigate these two questions. We recreate the 2014 IPC Optimal and Agile tracks on two distinct hardware environments and eight distinct software environments. We show that solver performance varies significantly based on the hardware and software environment, and that this variation is not equal for all planners. Furthermore, the observed variation is sufficient to change the competition rankings, including the top-ranked planners for some tracks.

Artificial intelligence in medicine, Jan 7, 2018

Radiological reporting generates a large amount of free-text clinical narratives, a potentially v... more Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. The resu...

International Journal on Artificial Intelligence Tools, 2017

Combining the complementary strengths of several algorithms through portfolio approaches has been... more Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Combining the complementary strengths of several algorithms through portfolio approaches has been... more Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for (domain-independent) optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive experimental analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

The ParLPG planning system is based on the idea of using a generic algorithm configuration proced... more The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure-here, the well-known ParamILS framework-to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain. This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10 17 possible configurations. A recent, largescale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains.

In the recent years the field of automated plan generation has significantly advanced and several... more In the recent years the field of automated plan generation has significantly advanced and several powerful domain-independent planners have been developed. However, no one of these systems clearly outperforms all the others in every known benchmark domain. It would then be useful to have a multi-planner system capable of automatically selecting and combining the most efficient planning technique(s) for each given domain. In this paper we propose a planner, called PbP (Portfolio-based Planner), which automatically configures a portfolio of existing planners, possibly using a useful set of macro-actions for each of them. The configuration relies on some knowledge about the performance of the planners in the portfolio and the observed usefulness of sets of macro-actions, which is automatically generated by a statistical analysis considering a set of training problems for the domain under consideration. The configuration knowledge for the given domain consists of a promising combination of planners in the portfolio, each one with a (possibly empty) set of macro-actions, and additional information specializing their round-robin scheduling at planning time. PbP has two variants, one focusing on speed (PbP.s) and one on plan quality (PbP.q). A preliminary version of PbP.s entered the learning track of the sixth IPC, and was the overall winner of this competition track. An experimental analysis presented in the paper confirms the effectiveness of PbP.s, indicates that PbP.q performs better than the IPC6 planners, shows that the learned configuration knowledge can be very useful for PbP.s/q, and demonstrates that PbP.s/q can perform much better than the basic planners forming the portfolio.

Lecture Notes in Computer Science, 2001

GPG is a planner based on planning graphs that combines local search and backtracking techniques ... more GPG is a planner based on planning graphs that combines local search and backtracking techniques for solving both plan-generation and plan-adaptation tasks. The space of the local search is formed by particular subgraphs of a planning graph representing partial plans. The operators for moving from one search state to the next one are graph modification operations corresponding to adding (deleting)

We show that by inferring parametex domains of planning operators, given the definitions of the o... more We show that by inferring parametex domains of planning operators, given the definitions of the operators and the initial and goal conditions, we can often speed up the planning process. We infer parameter domains by a polynomial-time algorithm that uses forward propagation of sets of constants occurring in the initial conditions and in operator postconditions. During planning parameter domains can be used to prune operator instances whose parameter domains are inconsistent with binding constraints, and to eliminate spurious "clobbering threats" that cannot, in fact, be realized without violating domain constraints. We illustrate these applications with examples from the UCPOP test suite and from the Rochester TRAINS transportation planning domain.

PROCEEDINGS OF THE NATIONAL …, 2000

DISCOPLAN is an implemented set of efficient preplanning algorithms intended to enable faster dom... more DISCOPLAN is an implemented set of efficient preplanning algorithms intended to enable faster domain-independent planning. It includes algorithms for discovering state constraints (invariants) that have been shown to be very useful, for example, for speeding up SAT-based planning. DISCOPLAN originally discovered only certain types of implicative constraints involving up to two fluent literals and any number of static literals, where one of the fluent literals contains all of the variables occurring in the other literals; only planning domains with STRIPS-like operators were handled. We have now extended DISCOPLAN in several directions. We describe new techniques that handle operators with conditional effects, and enable discovery of several new types of constraints. Moreover, discovered constraints can be fed back into the discovery process to obtain additional constraints. Finally, we outline unimplemented (but provably correct) methods for discovering additional types of constraints, including constraints involving arbitrarily many fluent literals.

The ParLPG planning system is based on the idea of using a generic algorithm configuration proced... more The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure-here, the well-known ParamILS framework-to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain. This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10 17 possible configurations. A recent, largescale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains.

PROCEEDINGS OF THE …, 2005

The ability to express "derived predicates" in the formaliza- tion of a planning domain... more The ability to express "derived predicates" in the formaliza- tion of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of "Rule-Action Graphs", particular graphs of actions and rules representing derived predicates. We present some techniques for representing rules and reasoning with them,

Future Internet

During the COVID-19 pandemic, the corporate online training sector has increased exponentially an... more During the COVID-19 pandemic, the corporate online training sector has increased exponentially and online course providers had to implement innovative solutions to be more efficient and provide a satisfactory service. This paper considers a real case study in implementing a chatbot, which answers frequently asked questions from learners on an Italian e-learning platform that provides workplace safety courses to several business customers. Having to respond quickly to the increase in the courses activated, the company decided to develop a chatbot using a cloud-based service currently available on the market. These services are based on Natural Language Understanding (NLU) engines, which deal with identifying information such as entities and intentions from the sentences provided as input. To integrate a chatbot in an e-learning platform, we studied the performance of the intent recognition task of the major NLU platforms available on the market with an in-depth comparison, using an I...

Discovering the effect of the simultaneous assumption of drugs is a very important field in medic... more Discovering the effect of the simultaneous assumption of drugs is a very important field in medical research that could improve the effectiveness of healthcare and avoid adverse drug reactions which can cause health problems to patients. Although there are several pharmacological databases containing information on drugs, this type of information is often expressed in the form of free text. Analyzing sentences in order to extract drug-drug interactions was the objective of the DDIExtraction-2013 task. Despite the fact that the challenge took place six years ago, the interest on this task is still active and several new methods based on Recurrent Neural Networks and Attention Mechanisms have been designed. In this paper, we propose a model that combines bidirectional Long Short Term Memory (LSTM) networks with the Self-Interaction Attention Mechanism. Experimental analysis shows how this model improves the classification accuracy reducing the tendency to predict the majority class re...

An estimated 20-25% of women experience sexual assault while at college. In response, institution... more An estimated 20-25% of women experience sexual assault while at college. In response, institutions of higher education are improving their policies and working to educate students on the issue. The purpose of this study is to examine whether undergraduate students at Duke University know and understand the University's Student Sexual Misconduct Policy on consent and sexual violence. Data gathered from student surveys (n = 320) yielded mixed results on respondents' knowledge of the policy. In addition, though students had greater understanding of sexual violence than hypothesized, respondents lacked understanding of the role of alcohol in consent. Statistical analyses showed that men, varsity athletes, freshmen and non-LGBTQ students were more likely to misunderstand sexual violence, as measured through responses to scenario questions on the student survey. Recommendations of this study to the University include adding information on alcohol and consent to the Student Sexual Misconduct Policy, improving outreach and follow up for educational programming, and implementing scenario questions in future surveys and training materials.

In multi-agent planning, preserving the agents' privacy has become an increasingly popular re... more In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is...

Fundamenta Informaticae, 2011

Planning through local search and action graphs is a powerful approach to fully-automated plannin... more Planning through local search and action graphs is a powerful approach to fully-automated planning which is implemented in the well-known LPG planner. The approach is based on a stochastic local search procedure exploring a space of partial plans and several heuristic features with different possible options. In this paper, we experimentally analyze the most important of them, with the goal of understanding and evaluating their impact on the performance of LPG, and of identifying default settings that work well on a large class of problems. In particular, we analyze several heuristic techniques for (a) evaluating the search neighborhood, (b) defining/restricting the search neighborhood, (c) selecting the next plan flaw to handle, (d) setting the “noise” parameter randomizing the search, and (e) computing reachability information that can be exploited by the heuristic functions used to evaluate the neighborhood elements. Some of these techniques were introduced in previous work on LPG, while others are new. Additional experimental results indicate that the current version of LPG using the identified best heuristic techniques as the default settings is competitive with the winner of the last (2008) International Planning Competition.

Journal of Artificial Intelligence Research, 2014

In the field of domain-independent planning, several powerful planners implementing different tec... more In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won t...

Journal of Artificial Intelligence Research, 1996

We propose some domain-independent techniques for bringing well-founded partial-order planners cl... more We propose some domain-independent techniques for bringing well-founded partial-order planners closer to practicality. The first two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by UCPOP to select plans for refinement. The other is based on preferring ``zero commitment'' (forced) plan refinements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the definitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modifications of UCPOP, our improved plan and...

Journal of Artificial Intelligence Research, 2006

The treatment of exogenous events in planning is practically important in many real-world domains... more The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When actions have durations, handling such temporal constraints adds an extra difficulty to planning. We propose an approach to planning in these domains which integrates constraint-based temporal reasoning into a graph-based planning framework using local search. Our techniques are implemented in a planner that took part in the 4th International Planning Competition (IPC-4). A statistical analysis of the results of IPC-4 demonstrates the effectiveness of our approach in terms of both CPU-time and plan quality. Additional experiments show the good performance of the temporal reasoning techniques int...

AI Communications, 2018

Solver competitions have been used in many areas of AI to assess the current state of the art and... more Solver competitions have been used in many areas of AI to assess the current state of the art and guide future research and development. AI planning is no exception, and the International Planning Competition (IPC) has been frequently run for nearly two decades. Due to the organisational and computational burden involved in running these competitions, solvers are generally compared using a single homogeneous hardware and software environment for all competitors. To what extent does the specific choice of hardware and software environment have an effect on solver performance, and is that effect distributed equally across the competing solvers? In this work, we use the competing planners and benchmark instance sets from the 2014 IPC to investigate these two questions. We recreate the 2014 IPC Optimal and Agile tracks on two distinct hardware environments and eight distinct software environments. We show that solver performance varies significantly based on the hardware and software environment, and that this variation is not equal for all planners. Furthermore, the observed variation is sufficient to change the competition rankings, including the top-ranked planners for some tracks.

Artificial intelligence in medicine, Jan 7, 2018

Radiological reporting generates a large amount of free-text clinical narratives, a potentially v... more Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. The resu...

International Journal on Artificial Intelligence Tools, 2017

Combining the complementary strengths of several algorithms through portfolio approaches has been... more Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Combining the complementary strengths of several algorithms through portfolio approaches has been... more Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for (domain-independent) optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive experimental analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.

The ParLPG planning system is based on the idea of using a generic algorithm configuration proced... more The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure-here, the well-known ParamILS framework-to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain. This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10 17 possible configurations. A recent, largescale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains.

In the recent years the field of automated plan generation has significantly advanced and several... more In the recent years the field of automated plan generation has significantly advanced and several powerful domain-independent planners have been developed. However, no one of these systems clearly outperforms all the others in every known benchmark domain. It would then be useful to have a multi-planner system capable of automatically selecting and combining the most efficient planning technique(s) for each given domain. In this paper we propose a planner, called PbP (Portfolio-based Planner), which automatically configures a portfolio of existing planners, possibly using a useful set of macro-actions for each of them. The configuration relies on some knowledge about the performance of the planners in the portfolio and the observed usefulness of sets of macro-actions, which is automatically generated by a statistical analysis considering a set of training problems for the domain under consideration. The configuration knowledge for the given domain consists of a promising combination of planners in the portfolio, each one with a (possibly empty) set of macro-actions, and additional information specializing their round-robin scheduling at planning time. PbP has two variants, one focusing on speed (PbP.s) and one on plan quality (PbP.q). A preliminary version of PbP.s entered the learning track of the sixth IPC, and was the overall winner of this competition track. An experimental analysis presented in the paper confirms the effectiveness of PbP.s, indicates that PbP.q performs better than the IPC6 planners, shows that the learned configuration knowledge can be very useful for PbP.s/q, and demonstrates that PbP.s/q can perform much better than the basic planners forming the portfolio.

Lecture Notes in Computer Science, 2001

GPG is a planner based on planning graphs that combines local search and backtracking techniques ... more GPG is a planner based on planning graphs that combines local search and backtracking techniques for solving both plan-generation and plan-adaptation tasks. The space of the local search is formed by particular subgraphs of a planning graph representing partial plans. The operators for moving from one search state to the next one are graph modification operations corresponding to adding (deleting)

We show that by inferring parametex domains of planning operators, given the definitions of the o... more We show that by inferring parametex domains of planning operators, given the definitions of the operators and the initial and goal conditions, we can often speed up the planning process. We infer parameter domains by a polynomial-time algorithm that uses forward propagation of sets of constants occurring in the initial conditions and in operator postconditions. During planning parameter domains can be used to prune operator instances whose parameter domains are inconsistent with binding constraints, and to eliminate spurious "clobbering threats" that cannot, in fact, be realized without violating domain constraints. We illustrate these applications with examples from the UCPOP test suite and from the Rochester TRAINS transportation planning domain.

PROCEEDINGS OF THE NATIONAL …, 2000

DISCOPLAN is an implemented set of efficient preplanning algorithms intended to enable faster dom... more DISCOPLAN is an implemented set of efficient preplanning algorithms intended to enable faster domain-independent planning. It includes algorithms for discovering state constraints (invariants) that have been shown to be very useful, for example, for speeding up SAT-based planning. DISCOPLAN originally discovered only certain types of implicative constraints involving up to two fluent literals and any number of static literals, where one of the fluent literals contains all of the variables occurring in the other literals; only planning domains with STRIPS-like operators were handled. We have now extended DISCOPLAN in several directions. We describe new techniques that handle operators with conditional effects, and enable discovery of several new types of constraints. Moreover, discovered constraints can be fed back into the discovery process to obtain additional constraints. Finally, we outline unimplemented (but provably correct) methods for discovering additional types of constraints, including constraints involving arbitrarily many fluent literals.