To provide, anywhere and anytime, smart assistive services to people, cognitive robots and agents need to be endowed with advanced spatio-temporal knowledge representation and reasoning capabilities. In this paper, a semantic approach for cloud-assisted robotics integrating entities of the ambient environment is proposed. Its principle consists of advanced contextual knowledge representation and reasoning models based on the hybridization of metric, topological and semantic information. A scenario dedicated to the cognitive assistance of frail people is implemented and analyzed for validation purposes of the proposed approach.

In this paper, the problem of endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects and moods of humans, in their context, is studied. A hybrid approach based on multilayer perceptron (MLP) neural network and n-ary ontologies for emotion-aware robotic systems is proposed. In particular, an algorithm based on the hybrid-level fusion, an expressive emotional knowledge representation and reasoning model are introduced to recognize complex and non-observable emotional context of the user. Empirical experiments on real-world dataset corroborate its effectiveness.

Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method -- Alpha-Beta search for durative moves-- that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable -- opening the door for a promising line of research on opponent combat modelling.

In order to automatically generate high-quality game levels, one needs to be able to automatically verify that the levels are playable. The simulation-based approach to playability testing uses an artificial agent to play through the level, but building such an agent is not always an easy task and such an agent is not always readily available. We discuss this prob- lem in the context of the physics-based puzzle game Cut the Rope, which features continuous time and state space, mak- ing several approaches such as exhaustive search and reactive agents inefficient. We show that a deliberative Prolog-based agent can be used to suggest all sensible moves at each state, which allows us to restrict the search space so that depth-first search for solutions become viable. This agent is successfully used to test playability in Ropossum, a level generator based on grammatical evolution. The method proposed in this paper is likely to be useful for a large variety of games with similar characteristics.

Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a temporal, modal logic provides more expressivity than languages historically associated with story generation, while still affording reasoning capabilities. We show that these properties enable certain aspects of narrative discourse generation by using two examples from different genres, and claim that this generalizes to a broader class of problems.

Standard game tree search algorithms, such as minimax or Monte Carlo Tree Search, assume the existence of an accurate forward model that simulates the effects of actions in the game. Creating such model, however, is a challenge in itself.One cause of the complexity of the task is the gap in level of abstraction between the informal specification of the model and its implementation language. To overcome this issue, we propose a technique for the implementation of forward models that relies on the Answer Set Programming paradigm and on well-established knowledge representation techniques from defeasible reasoning and reasoning about actions and change. We evaluate our approach in the context of Real-Time Strategy games using a collection of StarCraft scenarios.

Procedurally generating rich, naturally behaving AI-controlled video game characters is an important open problem. In this paper we focus on a particular aspect of non-playable character (NPC) behavior, long favored by science-fiction writers. Specifically, we study the effects of self-knowledge on NPC behavior. To do so we adopt the well-known framework of agent-centered real-time heuristic search applied to the standard pathfinding task on video-game maps. Such search agents normally use a heuristic function to guide them around a map to the goal state.

What characters believe, how they act based on those beliefs,and how their beliefs are updated is an essential element of many stories. State-space narrative planning algorithms treat their search spaces like a set of temporally possible worlds. We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is generated. We also present the results of an experiment that demonstrates that the model meets the expectations of a human audience.

In java linkedlist is implemented in Collections framework and you just need to import in your file a d use it all features. Import Collections.LinkedList; By using this you can import the Collections framework Linked list is three type 1.single 2.doubley 3.circular(by using single or double) Syntax: 1. LinkedList variable_name new ArrayList(); Here you are creating a linked list of name variable_name you use int for integer numbers Here you don't need to care about references which is automatically handle by the framework. Same as you can use doubley linked list In the given below Image you can understand the single linked list concept how the rferences works . In the single linked list two things are present in one node 1.data 2.reference of next node This same as self refrencial structure in c In the collections.framwork