Humans decompose tasks by trading off utility and computational cost

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior

Integrating planning and scheduling in workflow domains

One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as business process reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models. As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates planning and scheduling, IPSS. We present results in a real workflow domain, the telephone installation (TI) domain.The SHAMASH project has being carried out in the course of the R&D project funded by the Esprit Program of the Commission of the European Communities as project number 25491. A complementary grant was given by the Spanish research commission, CICYT, under project number TIC98-1847-CE. We thank the partners of this project, who have originated and contributed to the ideas reported: UF (Unio´n Fenosa), SAGE (Software AG Espan˜ a), SEMA GROUP sae, UC3M (Universidad Carlos III de Madrid), WIP (Wirstchaft und infrastruktur & Co Planungs KG), and EDP (Electricidade de Portugal). We would specially like to thank all the UC3M team, the PLANET people and Paul Kearney (BT). Through talks with him we have outlined many ideas. This work has also been partially funded by grant MCyT TIC2002-04146-C05-05 and the UAH project PI2005/084.Publicad

RoboCup@Home: commanding a service robot by natural language.

It was in the ancient Greece that myths were written and, among already there one could nd the human desire of robotic servants. It was Hephaestus, god of technology, blacksmiths, craftsmen and artisans who is said to have built robots to help him on his workshop. This show how deep in our thoughts was this desire that one could nd stories and tales of human-shaped machines that danced in china or inanimate materials like mud that gave shape to golems in Jewish tradition. In the renaissance, a lot of automata began to arise, beginning by Leonardo Da Vinci to the artisans from China and Japan, mankind was trying to produce automatic machines, sometimes for their own bene t, some other times to their delight and fascination. But it wasn't until the digital era that the dream began to seem feasible. After millennia of wondering of automated robots, computers showed that automatic calculus was possible and from this, ideas of an automated mind arose. Theories for cognitive architectures are born since the early stages of arti cial intelligence, cognitive architectures that now are a reality. Thanks to the technological advances and the knowledge about the mind, what once was material for ctional tales, now is feasible and only matter of time. There is a lot of research on robotics and cognition that is beginning to get coupled into what are called "service robots". In this thesis, I present a system that participates in a competition designed for this kind of robots. A competition that have on its basis the same dream that humans have had all around the world for centuries: the cohabitation of humans and service automatons

Optimal Planning Modulo Theories

Planning for real-world applications requires algorithms and tools with the ability to handle the complexity such scenarios entail. However, meeting the needs of such applications poses substantial challenges, both representational and algorithmic. On the one hand, expressive languages are needed to build faithful models. On the other hand, efficient solving techniques that can support these languages need to be devised. A response to this challenge is underway, and the past few years witnessed a community effort towards more expressive languages, including decidable fragments of first-order theories. In this work we focus on planning with arithmetic theories and propose Optimal Planning Modulo Theories, a framework that attempts to provide efficient means of dealing with such problems. Leveraging generic Optimization Modulo Theories (OMT) solvers, we first present domain-specific encodings for optimal planning in complex logistic domains. We then present a more general, domain- independent formulation that allows to extend OMT planning to a broader class of well-studied numeric problems in planning. To the best of our knowledge, this is the first time OMT procedures are employed in domain-independent planning

Using features for automated problem solving

We motivate and present an architecture for problem solving where an abstraction layer of "features" plays the key role in determining methods to apply. The system is presented in the context of theorem proving with Isabelle, and we demonstrate how this approach to encoding control knowledge is expressively different to other common techniques. We look closely at two areas where the feature layer may offer benefits to theorem proving — semi-automation and learning — and find strong evidence that in these particular domains, the approach shows compelling promise. The system includes a graphical theorem-proving user interface for Eclipse ProofGeneral and is available from the project web page, http://feasch.heneveld.org

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

A PROCESS ANALYSIS OF ENGINEERING PROBLEM SOLVING AND ASSESSMENT OF PROBLEM SOLVING SKILLS

In the engineering profession, one of the most critical skills to possess is accurate and efficient problem solving. Thus, engineering educators should strive to help students develop skills needed to become competent problem solvers. In order to measure the development of skills, it is necessary to assess student performance, identify any deficiencies present in problem solving attempts, and identify trends in performance over time. Through iterative assessment using standard assessment metrics, researchers/instructors are able to track trends in problem solving performance across time, which can serve as a gauge of students\u27 learning gains. This research endeavor studies the problem solving process of first year engineering students in order to assess how person and process factors influence problem-solving success. This research makes a contribution to the literature in engineering education by 1) providing a coding scheme that can be used to analyze problem solving attempts in terms of the process rather than just outcomes, 2) providing an assessment tool which can be used to measure performance along the seven stage problem solving cycle, and 3) describing the effects of person and process factors on problem solving performance

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics