Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming. Written by two of the leading researchers of this engaging field, this book will surely serve as THE reference for researchers in the fastest-growing area of computer science, and be used as a text for advanced undergraduate or graduate courses.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances In this article, we review recent developments in recommender systems and discuss the major challenges We compare and evaluate available algorithms and examine their roles in the future developments In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems Potential impacts and future directions are discussed We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers

Recommender Systems

A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.

Facing the cold start problem in recommender systems

Recommender systems for automatically suggested items of interest to users have become increasingly essential in fields where mass personalization is highly valued. The popular core techniques of such systems are collaborative filtering, content-based filtering and combinations of these. In this paper, we discuss hybrid approaches, using collaborative and also content data to address cold-start - that is, giving recommendations to novel users who have no preference on any items, or recommending items that no user of the community has seen yet. While there have been lots of studies on solving the item-side problems, solution for user-side problems has not been seen public. So we develop a hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information. The experiments with MovieLen data indicate substantial and consistent improvements of this model in overcoming the cold-start user-side problem.

Addressing cold-start problem in recommendation systems

Knockout tournaments constitute a common format of sporting events, and also model a specific type of election scheme (namely, sequential pairwise elimination election). In such tournaments the designer controls the shape of the tournament (a binary tree) and the seeding of the players (their assignment to the tree leaves). In this paper we investigate the computational complexity of tournament schedule control, i.e., designing a tournament that maximizes the winning probability a target player. We start with a generic probabilistic model consisting of a matrix of pairwise winning probabilities, and then investigate the problem under two types of constraint: constraints on the probability matrix, and constraints on the allowable tournament structure. While the complexity of the general problem is as yet unknown, these various constraints -- all naturally occurring in practice -- serve to push to the problem to one side or the other: easy (polynomial) or hard (NP-complete).

/pdf/on-the-complexity-of-schedule-control-problems-for-knockout-187r2tkx2s.pdf

On the complexity of schedule control problems for knockout tournaments

We present a method for synthesizing animations of autonomous space, water, and land-based vehicles in games or other interactive simulations. Controlling the motion of such vehicles to achieve a desirable behavior is difficult due to the constraints imposed by the system dynamics. We combine real-time path planning and a simplified physics model to automatically compute control actions to drive a vehicle from an input state to desirable output states based on a behavior cost function. Both offline trajectory preprocessing and online search are used to build an animation framework suitable for interactive vehicle simulations. We demonstrate synthesized animations of spacecraft performing a variety of autonomous behaviors, including Seek, Pursue, Avoid, Avoid Collision, and Flee. We also explore several enhancements to the basic planning algorithm and examine the resulting tradeoffs in runtime performance and quality of the generated motion.

/pdf/autonomous-behaviors-for-interactive-vehicle-animations-1lj0c916jn.pdf

Autonomous behaviors for interactive vehicle animations

We investigated the existence of fair seeding in knockout tournaments. We define two fairness criteria, both adapted from the literature: envy-freeness and order preservation. We show how to achieve the first criterion in tournaments whose structure is unconstrained, and prove an impossibility result for balanced tournaments. For the second criterion we have a similar result for unconstrained tournaments, but not for the balanced case. We provide instead a heuristic algorithm which we show through experiments to be efficient and effective. This suggests that the criterion is achievable also in balanced tournaments. However, we prove that it again becomes impossible to achieve when we add a weak condition guarding against the phenomenon of tournament dropout.

/pdf/fair-seeding-in-knockout-tournaments-3an9b4srpv.pdf

Fair Seeding in Knockout Tournaments

Research in multi-agent systems has led to the development of many multi-agent control architectures. However, we believe that there is currently no known optimal structure for multi-agent control since the effectiveness of any particular architecture varies depending on the domain of the problem. Therefore, deployment of multi-agent teams would be significantly sped up by a development and deployment environment which would allow designers to easily modify the architecture. In this paper, we present a flexible team-oriented programming and execution architecture, MONAD, which integrates hierarchical behavior-based control, multi-agent coordination mechanisms, and agent-task allocation services. MONAD uses a novel scripting language that allows designers to easily modify the team structure, behavior hierarchy, applicability conditions, and arbitration methods, in pursuit of the best solution for a particular problem. We have evaluated the MONAD architecture within a well-accepted adversarial game environment, GameBots, to enable qualitative comparison of different control techniques. In this environment, we were able to rapidly design and test several teams of agents who used role, preference, or a combination of role and preference arbitration and observed that these different teams varied in their performance characteristics.

https://u.cs.biu.ac.il/~galk/Publications/Papers/aamas03.pdf

MONAD: a flexible architecture for multi-agent control

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