Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.Comment: AAAI 202

Social and Technological Efficiency of Patent Systems

This article develops an evolutionary model of industry dynamics in order to carry out a richer theoretical analysis of the consequences of a stronger patent system. The first results obtained in our article are rather consistent with the anti-patent arguments and they do not favour the case for a stronger patent system: higher social welfare and technical progress are observed in our model in industries with milder patent systems (lower patent height and patent life).Innovation, technical progress, patent system, Intellectual property rights,

Technological and Social Costs and Benefits of Patent Systems

"If we did not have a patent system, it would be irresponsible, on the basis of our present knowledge of its economic consequences, to recommend instituting one. But since we have had a patent system for a long time, it would be irresponsible, on the basis of our present knowledge, to recommend abolishing it." Machlup (1958) - cited by Hall (2002) The demand for a stronger patenting system has become in the recent period a major source of tension between the U.S. government and the E.U. The US demand is generally motivated by the conventional economic wisdom affirming that a strong patenting system yields convenient incentives for the private investment in Research and Development (R&D) and hence, for technical progress in Society. This rather mechanistic approach of technological dynamics and of the role of the patenting is mainly based on the neoclassical theory of technical progress that strongly focuses on the agents' incentives rather than on the dynamics of the existent technological systems. Other appreciations of the existing patenting systems have nevertheless continued to be quite critical (see Machlup (1958) and Penrose (1951)). These appreciations are generally based on approaches where the nature of the actual technologies plays a central role. Moreover, the first part of the opinion emitted by Machlup in the above excerpt becomes very urgent since the question of establishing a strong patenting system is actually scrutinized for some industries in Europe (like the software industry) and in some countries (like Russia and China). We should hence consider the social costs of the patenting system, as well as its advantages, in order to guide such decisions. More specifically, it is time to seriously consider and check the old and new criticism of this system. The shortcomings of the standard wisdom have more recently been pointed out by Merges & Nelson (1990) and Mazzoleni & Nelson (1998). We propose to reassess the theoretical social value of patenting through a model founded on the approach adopted by these more empirical and conceptual studies. We develop a simulation model based on the Nelson & Winter (1982), part V. This basic model is completed by a patent system that allows the protection of the innovations. We therefore use this model for evaluating the efficiency of this system under different technological conditions emphasized by Merges & Nelson (1990) and as a function of different dimensions of patents (mainly their length and their breadth). An econometric study of the results from Monte Carlo simulations is used to evaluate the determinants of the Social costs and benefits of patents. These social effects are mainly characterized at two levels: at the level of the efficiency of the technical progress in the industry, and at the level of the social surplus. The neoclassical approaches conclude to a positive effect on both dimensions. Evolutionary approaches point at the contingency of these results with respect to the technological particularities of the industries. For example, Merges & Nelson (1990) distinguishes four classes of technologies in which the role of patents can be strongly contrasted: discrete inventions, cumulative technologies, chemical technologies and sciencebased technologies. We propose to include the specificities of these classes in our analysis, through different calibrations of the technology space of our industry dynamics model. The results of the simulations will then allow us to check the effectiveness of the patenting system in different configurations and with different characteristics measuring its strength. References Hall, B. (2002), "Current issues and trends in the economics of patents", Lecture to the ESSID Summer School in Industrial Dynamics Hall, B. & Ham Ziedonis, R. M. (2001), The effects of strengthening patent rights on firms engaged in cumulative innovation: Insights from the semiconductor industry, in G. Libecap, ed., "Entrepreneurial Inputs and Outcomes: New Studies of Entrepreneurship in the United States", Vol. 13 of Advances in the Study of Entrepreneurship, Innovation, and Economic Growth, Elsevier Science, Amsterdam. Jaffe, A. B. (2000), "The u.s. patent system in transition: Policy innovation and the innovation process", Research Policy 29, 531–557. Machlup, F. (1958), "An economic review of the patent system", Study No. 15 of Commission on Judiciary, Sub comm. on Patents, Trademarks, and Copyrights, 85th Congress, 2d Session. Mazzoleni, R. & Nelson, R. R. (1998), "The benefits and costs of strong patent protection: A contribution to the current debate", Research Policy 27, 273–284. Merges, R. & Nelson, R. R. (1990), "On the complex economics of patent scope", Columbia Law Review 90, 839–916. Nelson, R. R. & Winter, S. (1982), An Evolutionary Theory of Economic Change, The Belknap Press of Harvard University, London. Penrose, E. (1951), The Economics of the International Patent System, John Hopkins University Press, BaltimorePatent system, social welfare, public policy, intellectual property rights, industrial dynamics

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Money Down the (Brand) Drain : An exploration of the constraints of the adoption of brand strategies and the adversity facing investment in brands by Chinese suppliers

This paper explores the perception by Chinese marketing academics and consultants of benefits and constraints of the adoption of branding techniques. We explore the lived experience of Chinese branding experts to capture their experience of branding in China by means of 19 phenomenological interviews. There are differences between the Western and Chinese conception of brands, these in China serve a more social function and are associated with social processes. The Chinese economy has the characteristics of a large state economy and has large state-owned conglomerates which have strong brands as a result of Government support. Non-state owned businesses have much more difficulties innovating and building brands because of scarce resources. Business leaders in China seem to have a short term orientation, and there is evidence that they tend to select strategies based on imitation of leading brands, as well as that of manufacturing and marketing mass produced, low cost products. This proliferation of generic products and “me too” brands is complemented by a plethora of counterfeit goods. Chinese leaders do not have incentives to invest in long term innovation and brands, nor in brand management as they feel these investments cannot be protected from counterfeiting; at the same time, the competitive climate means that Chinese non-state owned companies need to be very responsive and achieve fast returns in order to survive. Policy makers should strengthen IPR protection legislation and counteract counterfeiting; foreign investors and local companies are advised to adopt mobile defense strategies for their brands

How Much Should Society Fuel the Greed of Innovators? On the Relations between Appropriability, Opportunities and Rates of Innovation

The paper attempts a critical assessment of both the theory and the empirical evidence on the role of appropriability and in particular of Intellectual Property Right (IPR) as incentives for technological innovation. We start with a critical discussion of the standard justification of the attribution of IPR in terms of "market failures" in knowledge generation. Such an approach we argue misses important features of technological knowledge and also neglects the importance of non-market institutions in the innovation process. Next, we examine the recent changes in the IPR regimes and their influence upon both rates of patenting and underlying rates of innovation. The evidence broadly suggests that, first, IPRs are not the most important device apt to "profit from innovation"; and second, they have at best no impact, or possibly even a negative impact on the underlying rates of innovation. Rather, we argued, technology- and industry-specific patterns of innovation are primarily driven by the opportunities associated with each technological paradigm. Conversely, firm-specific abilities to seize them and "profit from innovation" depend partly on adequacy of the strategic combinations identified by the taxonomy of Teece (1986) and partly on idiosyncratic capabilities embodied in the various firms.Appropriability, Intellectual Property Right, Innovation, Technological opportunities